paper_url stringlengths 36 81 | paper_title stringlengths 1 242 ⌀ | paper_arxiv_id stringlengths 9 16 ⌀ | paper_url_abs stringlengths 18 314 | paper_url_pdf stringlengths 21 935 ⌀ | repo_url stringlengths 26 200 | is_official bool 2 classes | mentioned_in_paper bool 2 classes | mentioned_in_github bool 2 classes | framework stringclasses 9 values |
|---|---|---|---|---|---|---|---|---|---|
https://paperswithcode.com/paper/odyssey-a-public-gpu-based-code-for-general | Odyssey: A Public GPU-Based Code for General-Relativistic Radiative Transfer in Kerr Spacetime | 1601.02063 | https://arxiv.org/abs/1601.02063v2 | https://arxiv.org/pdf/1601.02063v2.pdf | https://github.com/LeonGeiger/Kerr | false | false | true | none |
https://paperswithcode.com/paper/efficient-leave-one-out-cross-validation-for | Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models | 1810.10559 | https://arxiv.org/abs/1810.10559v5 | https://arxiv.org/pdf/1810.10559v5.pdf | https://github.com/paul-buerkner/psis-non-factorized-paper | true | true | false | none |
https://paperswithcode.com/paper/automatic-post-editing-of-machine-translation | Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach | null | https://aclanthology.org/D18-1341 | https://aclanthology.org/D18-1341.pdf | https://github.com/trangvu/ape-npi | false | false | false | tf |
https://paperswithcode.com/paper/attngan-fine-grained-text-to-image-generation | AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks | 1711.10485 | http://arxiv.org/abs/1711.10485v1 | http://arxiv.org/pdf/1711.10485v1.pdf | https://github.com/bprabhakar/text-to-image | false | false | false | pytorch |
https://paperswithcode.com/paper/photo-realistic-single-image-super-resolution | Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | 1609.04802 | http://arxiv.org/abs/1609.04802v5 | http://arxiv.org/pdf/1609.04802v5.pdf | https://github.com/2023-MindSpore-1/ms-code-210/tree/main/CSNL | false | false | false | mindspore |
https://paperswithcode.com/paper/distilling-interpretable-models-into-human | Distilling Interpretable Models into Human-Readable Code | 2101.08393 | https://arxiv.org/abs/2101.08393v2 | https://arxiv.org/pdf/2101.08393v2.pdf | https://github.com/google/pwlfit | true | true | false | none |
https://paperswithcode.com/paper/wide-residual-networks | Wide Residual Networks | 1605.07146 | http://arxiv.org/abs/1605.07146v4 | http://arxiv.org/pdf/1605.07146v4.pdf | https://github.com/epfl-ml-reproducers/subspace-attack-reproduction | false | false | true | pytorch |
https://paperswithcode.com/paper/show-and-tell-lessons-learned-from-the-2015 | Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge | 1609.06647 | http://arxiv.org/abs/1609.06647v1 | http://arxiv.org/pdf/1609.06647v1.pdf | https://github.com/HughKu/Im2txt | false | false | true | tf |
https://paperswithcode.com/paper/a-wavenet-for-speech-denoising | A Wavenet for Speech Denoising | 1706.07162 | http://arxiv.org/abs/1706.07162v3 | http://arxiv.org/pdf/1706.07162v3.pdf | https://github.com/francesclluis/source-separation-wavenet | false | false | true | tf |
https://paperswithcode.com/paper/stackgan-realistic-image-synthesis-with | StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks | 1710.10916 | http://arxiv.org/abs/1710.10916v3 | http://arxiv.org/pdf/1710.10916v3.pdf | https://github.com/Maymaher/StackGANv2 | false | false | true | pytorch |
https://paperswithcode.com/paper/towards-k-means-friendly-spaces-simultaneous | Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering | 1610.04794 | http://arxiv.org/abs/1610.04794v2 | http://arxiv.org/pdf/1610.04794v2.pdf | https://github.com/boyangumn/DCN | true | true | true | none |
https://paperswithcode.com/paper/simulaqron-a-simulator-for-developing-quantum | SimulaQron - A simulator for developing quantum internet software | 1712.08032 | http://arxiv.org/abs/1712.08032v2 | http://arxiv.org/pdf/1712.08032v2.pdf | https://github.com/quantumprotocolzoo/protocols | false | false | true | none |
https://paperswithcode.com/paper/recipenlg-a-cooking-recipes-dataset-for-semi | RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation | null | https://aclanthology.org/2020.inlg-1.4 | https://aclanthology.org/2020.inlg-1.4.pdf | https://github.com/Glorf/recipenlg | false | false | false | pytorch |
https://paperswithcode.com/paper/online-deep-learning-learning-deep-neural | Online Deep Learning: Learning Deep Neural Networks on the Fly | 1711.03705 | http://arxiv.org/abs/1711.03705v1 | http://arxiv.org/pdf/1711.03705v1.pdf | https://github.com/LIBOL/ODL | false | false | true | pytorch |
https://paperswithcode.com/paper/model-rubik-s-cube-twisting-resolution-depth | Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets | 2010.14819 | https://arxiv.org/abs/2010.14819v2 | https://arxiv.org/pdf/2010.14819v2.pdf | https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mobilenetv3_family | false | false | false | tf |
https://paperswithcode.com/paper/190600133 | ArcticNet: A Deep Learning Solution to Classify Arctic Wetlands | 1906.00133 | https://arxiv.org/abs/1906.00133v1 | https://arxiv.org/pdf/1906.00133v1.pdf | https://github.com/geekJZY/arcticnet | true | true | false | pytorch |
https://paperswithcode.com/paper/multi-label-image-classification-via | Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection | 1809.05884 | http://arxiv.org/abs/1809.05884v2 | http://arxiv.org/pdf/1809.05884v2.pdf | https://github.com/Yochengliu/MLIC-KD-WSD | false | false | true | none |
https://paperswithcode.com/paper/few-shot-learning-with-graph-neural-networks | Few-Shot Learning with Graph Neural Networks | 1711.04043 | http://arxiv.org/abs/1711.04043v3 | http://arxiv.org/pdf/1711.04043v3.pdf | https://github.com/HoganZhang/few-shot-gnn | false | false | true | pytorch |
https://paperswithcode.com/paper/generative-adversarial-networks | Generative Adversarial Networks | 1406.2661 | https://arxiv.org/abs/1406.2661v1 | https://arxiv.org/pdf/1406.2661v1.pdf | https://github.com/syahdeini/gan | false | false | true | tf |
https://paperswithcode.com/paper/mastering-chess-and-shogi-by-self-play-with-a | Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm | 1712.01815 | http://arxiv.org/abs/1712.01815v1 | http://arxiv.org/pdf/1712.01815v1.pdf | https://github.com/Neo-The1/ThinkingTicTacToe | false | false | true | tf |
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection | Focal Loss for Dense Object Detection | 1708.02002 | http://arxiv.org/abs/1708.02002v2 | http://arxiv.org/pdf/1708.02002v2.pdf | https://github.com/trongnghia00/darknet | false | false | true | none |
https://paperswithcode.com/paper/semi-supervised-learning-with-ladder-networks | Semi-Supervised Learning with Ladder Networks | 1507.02672 | http://arxiv.org/abs/1507.02672v2 | http://arxiv.org/pdf/1507.02672v2.pdf | https://github.com/brandonrobertz/AcademicUrlTitles | false | false | true | none |
https://paperswithcode.com/paper/co-designing-for-a-hybrid-workplace | Co-designing for a Hybrid Workplace Experience in Software Development | 2212.09638 | https://arxiv.org/abs/2212.09638v1 | https://arxiv.org/pdf/2212.09638v1.pdf | https://github.com/co-design-hybrid/co-design-hybrid | true | true | false | none |
https://paperswithcode.com/paper/towards-automated-deep-learning-efficient | Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search | 1807.06906 | http://arxiv.org/abs/1807.06906v1 | http://arxiv.org/pdf/1807.06906v1.pdf | https://github.com/arberzela/EfficientNAS | false | false | true | pytorch |
https://paperswithcode.com/paper/statistical-parametric-speech-synthesis-using | Statistical Parametric Speech Synthesis Using Generative Adversarial Networks Under A Multi-task Learning Framework | 1707.01670 | http://arxiv.org/abs/1707.01670v2 | http://arxiv.org/pdf/1707.01670v2.pdf | https://github.com/rickyHong/GANTTS-update-repl | false | false | true | pytorch |
https://paperswithcode.com/paper/perfect-sampling-with-unitary-tensor-networks | Perfect Sampling with Unitary Tensor Networks | 1201.3974 | http://arxiv.org/abs/1201.3974v3 | http://arxiv.org/pdf/1201.3974v3.pdf | https://github.com/0/itensor-linear-rotors | false | false | true | none |
https://paperswithcode.com/paper/real-time-single-image-and-video-super | Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network | 1609.05158 | http://arxiv.org/abs/1609.05158v2 | http://arxiv.org/pdf/1609.05158v2.pdf | https://github.com/Nhat-Thanh/ESPCN-Pytorch | false | false | true | pytorch |
https://paperswithcode.com/paper/a-structured-matrix-factorization-framework | A structured matrix factorization framework for large scale calcium imaging data analysis | 1409.2903 | http://arxiv.org/abs/1409.2903v1 | http://arxiv.org/pdf/1409.2903v1.pdf | https://github.com/YGUO29/FANTASIA-CaImAn | false | false | true | none |
https://paperswithcode.com/paper/recent-trends-in-deep-learning-based-natural | Recent Trends in Deep Learning Based Natural Language Processing | 1708.02709 | http://arxiv.org/abs/1708.02709v8 | http://arxiv.org/pdf/1708.02709v8.pdf | https://github.com/ridakadri14/AspectBasedSentimentAnalysis | false | false | true | tf |
https://paperswithcode.com/paper/cvae-gan-fine-grained-image-generation | CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training | 1703.10155 | http://arxiv.org/abs/1703.10155v2 | http://arxiv.org/pdf/1703.10155v2.pdf | https://github.com/One-sixth/CVAE-GAN_tensorlayer | false | false | true | tf |
https://paperswithcode.com/paper/liqui-a-software-design-architecture-and | LIQUi|>: A Software Design Architecture and Domain-Specific Language for Quantum Computing | 1402.4467 | http://arxiv.org/abs/1402.4467v1 | http://arxiv.org/pdf/1402.4467v1.pdf | https://github.com/hhy37/Liquid | false | false | true | none |
https://paperswithcode.com/paper/quantum-algorithm-for-solving-linear-systems | Quantum algorithm for solving linear systems of equations | 0811.3171 | http://arxiv.org/abs/0811.3171v3 | http://arxiv.org/pdf/0811.3171v3.pdf | https://github.com/hhy37/Liquid | false | false | true | none |
https://paperswithcode.com/paper/automatic-inference-of-sound-correspondence | Automatic Inference of Sound Correspondence Patterns across Multiple Languages | null | https://aclanthology.org/J19-1004 | https://aclanthology.org/J19-1004.pdf | https://github.com/lingpy/correspondence-pattern-paper | true | true | false | none |
https://paperswithcode.com/paper/the-temporal-event-graph | The Temporal Event Graph | 1706.02128 | http://arxiv.org/abs/1706.02128v1 | http://arxiv.org/pdf/1706.02128v1.pdf | https://github.com/empiricalstateofmind/eventgraphs | false | false | true | none |
https://paperswithcode.com/paper/inverse-problems-in-asteroseismology | Inverse Problems in Asteroseismology | 1808.06649 | http://arxiv.org/abs/1808.06649v1 | http://arxiv.org/pdf/1808.06649v1.pdf | https://github.com/earlbellinger/thesis | false | false | true | none |
https://paperswithcode.com/paper/predictive-entropy-search-for-efficient | Predictive Entropy Search for Efficient Global Optimization of Black-box Functions | 1406.2541 | http://arxiv.org/abs/1406.2541v1 | http://arxiv.org/pdf/1406.2541v1.pdf | https://github.com/chongkewu/PESC-HPC | false | false | true | none |
https://paperswithcode.com/paper/tencent-ml-images-a-large-scale-multi-label | Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning | 1901.01703 | https://arxiv.org/abs/1901.01703v7 | https://arxiv.org/pdf/1901.01703v7.pdf | https://github.com/Tencent/tencent-ml-images | true | true | true | tf |
https://paperswithcode.com/paper/visual-relationship-detection-with-language-1 | Visual Relationship Detection with Language prior and Softmax | 1904.07798 | http://arxiv.org/abs/1904.07798v1 | http://arxiv.org/pdf/1904.07798v1.pdf | https://github.com/Jungjaewon/Visual-Relationship-Detection | false | false | true | caffe2 |
https://paperswithcode.com/paper/end-to-end-memory-networks | End-To-End Memory Networks | 1503.08895 | http://arxiv.org/abs/1503.08895v5 | http://arxiv.org/pdf/1503.08895v5.pdf | https://github.com/dare0021/MemN2N_Bench | false | false | true | none |
https://paperswithcode.com/paper/towards-high-performance-video-object | Towards High Performance Video Object Detection for Mobiles | 1804.05830 | http://arxiv.org/abs/1804.05830v1 | http://arxiv.org/pdf/1804.05830v1.pdf | https://github.com/stanlee321/LightFlow-TensorFlow | false | false | true | tf |
https://paperswithcode.com/paper/multimodal-word-distributions | Multimodal Word Distributions | 1704.08424 | https://arxiv.org/abs/1704.08424v2 | https://arxiv.org/pdf/1704.08424v2.pdf | https://github.com/benathi/multisense-prob-fasttext | false | false | true | none |
https://paperswithcode.com/paper/feature-importance-measure-for-non-linear | Feature Importance Measure for Non-linear Learning Algorithms | 1611.07567 | http://arxiv.org/abs/1611.07567v1 | http://arxiv.org/pdf/1611.07567v1.pdf | https://github.com/mcvidomi/MFI | false | false | true | none |
https://paperswithcode.com/paper/inference-of-stellar-parameters-from | Inference of stellar parameters from brightness variations | 1805.04519 | http://arxiv.org/abs/1805.04519v1 | http://arxiv.org/pdf/1805.04519v1.pdf | https://github.com/mkness/ACFCannon | true | true | false | none |
https://paperswithcode.com/paper/event-graphs-advances-and-applications-of | Event Graphs: Advances and Applications of Second-Order Time-Unfolded Temporal Network Models | 1809.03457 | http://arxiv.org/abs/1809.03457v1 | http://arxiv.org/pdf/1809.03457v1.pdf | https://github.com/empiricalstateofmind/eventgraphs | true | true | true | none |
https://paperswithcode.com/paper/separating-the-signal-from-the-noise-evidence | Separating the signal from the noise: Evidence for deceleration in old-age death rates | 1707.09433 | http://arxiv.org/abs/1707.09433v2 | http://arxiv.org/pdf/1707.09433v2.pdf | https://github.com/dfeehan/oldage-paper-code-released | false | false | true | none |
https://paperswithcode.com/paper/cnncnn-convolutional-decoders-for-image | CNN+CNN: Convolutional Decoders for Image Captioning | 1805.09019 | http://arxiv.org/abs/1805.09019v1 | http://arxiv.org/pdf/1805.09019v1.pdf | https://github.com/qingzwang/GHA-ImageCaptioning | false | false | true | pytorch |
https://paperswithcode.com/paper/spnets-differentiable-fluid-dynamics-for-deep | SPNets: Differentiable Fluid Dynamics for Deep Neural Networks | 1806.06094 | http://arxiv.org/abs/1806.06094v2 | http://arxiv.org/pdf/1806.06094v2.pdf | https://github.com/cschenck/SmoothParticleNets | true | true | false | pytorch |
https://paperswithcode.com/paper/deep-residual-learning-for-image-recognition | Deep Residual Learning for Image Recognition | 1512.03385 | http://arxiv.org/abs/1512.03385v1 | http://arxiv.org/pdf/1512.03385v1.pdf | https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute | false | false | false | mindspore |
https://paperswithcode.com/paper/efficient-estimation-of-word-representations | Efficient Estimation of Word Representations in Vector Space | 1301.3781 | http://arxiv.org/abs/1301.3781v3 | http://arxiv.org/pdf/1301.3781v3.pdf | https://github.com/palmagro/gg2vec | false | false | true | none |
https://paperswithcode.com/paper/a-structured-self-attentive-sentence | A Structured Self-attentive Sentence Embedding | 1703.03130 | http://arxiv.org/abs/1703.03130v1 | http://arxiv.org/pdf/1703.03130v1.pdf | https://github.com/hantek/SelfAttentiveSentEmbed | false | false | true | pytorch |
https://paperswithcode.com/paper/focal-loss-for-dense-object-detection | Focal Loss for Dense Object Detection | 1708.02002 | http://arxiv.org/abs/1708.02002v2 | http://arxiv.org/pdf/1708.02002v2.pdf | https://github.com/fizyr/keras-retinanet | false | false | true | tf |
https://paperswithcode.com/paper/yolo9000-better-faster-stronger | YOLO9000: Better, Faster, Stronger | 1612.08242 | http://arxiv.org/abs/1612.08242v1 | http://arxiv.org/pdf/1612.08242v1.pdf | https://github.com/vantupham/darknet | false | false | true | none |
https://paperswithcode.com/paper/u-net-convolutional-networks-for-biomedical | U-Net: Convolutional Networks for Biomedical Image Segmentation | 1505.04597 | http://arxiv.org/abs/1505.04597v1 | http://arxiv.org/pdf/1505.04597v1.pdf | https://github.com/muramasa8191/DeepLearning | false | false | true | tf |
https://paperswithcode.com/paper/sgdr-stochastic-gradient-descent-with-warm | SGDR: Stochastic Gradient Descent with Warm Restarts | 1608.03983 | http://arxiv.org/abs/1608.03983v5 | http://arxiv.org/pdf/1608.03983v5.pdf | https://github.com/Harshvardhan1/cyclic-learning-schedulers-pytorch | false | false | true | pytorch |
https://paperswithcode.com/paper/spatiotemporal-multiplier-networks-for-video | Spatiotemporal Multiplier Networks for Video Action Recognition | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Feichtenhofer_Spatiotemporal_Multiplier_Networks_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Feichtenhofer_Spatiotemporal_Multiplier_Networks_CVPR_2017_paper.pdf | https://github.com/feichtenhofer/st-resnet | true | true | false | none |
https://paperswithcode.com/paper/knowing-when-to-look-adaptive-attention-via-a | Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning | 1612.01887 | http://arxiv.org/abs/1612.01887v2 | http://arxiv.org/pdf/1612.01887v2.pdf | https://github.com/miroblog/AdaptiveAttention | false | false | true | pytorch |
https://paperswithcode.com/paper/salient-object-detection-driven-by-fixation | Salient Object Detection Driven by Fixation Prediction | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Salient_Object_Detection_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Salient_Object_Detection_CVPR_2018_paper.pdf | https://github.com/wenguanwang/ASNet | true | true | false | none |
https://paperswithcode.com/paper/supervised-learning-of-universal-sentence | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data | 1705.02364 | http://arxiv.org/abs/1705.02364v5 | http://arxiv.org/pdf/1705.02364v5.pdf | https://github.com/facebookresearch/InferSent | true | true | true | pytorch |
https://paperswithcode.com/paper/a-high-coverage-method-for-automatic-false | A High Coverage Method for Automatic False Friends Detection for Spanish and Portuguese | null | https://aclanthology.org/W18-3903 | https://aclanthology.org/W18-3903.pdf | https://github.com/pln-fing-udelar/false-friends | true | true | false | none |
https://paperswithcode.com/paper/the-chefs-hat-simulation-environment-for | The Chef's Hat Simulation Environment for Reinforcement-Learning-Based Agents | 2003.05861 | https://arxiv.org/abs/2003.05861v1 | https://arxiv.org/pdf/2003.05861v1.pdf | https://github.com/pablovin/MoodyFramework | false | false | true | none |
https://paperswithcode.com/paper/deep-video-deblurring | Deep Video Deblurring | 1611.08387 | http://arxiv.org/abs/1611.08387v1 | http://arxiv.org/pdf/1611.08387v1.pdf | https://github.com/susomena/DeepSlowMotion | false | false | true | tf |
https://paperswithcode.com/paper/adaptive-system-optimization-using-random | Adaptive system optimization using random directions stochastic approximation | 1502.05577 | http://arxiv.org/abs/1502.05577v2 | http://arxiv.org/pdf/1502.05577v2.pdf | https://github.com/prashla/RDSA | true | true | false | none |
https://paperswithcode.com/paper/identification-of-emergency-blood-donation | Identification of Emergency Blood Donation Request on Twitter | null | https://aclanthology.org/W18-5907 | https://aclanthology.org/W18-5907.pdf | https://github.com/pmathur5k10/EBDR | true | true | false | none |
https://paperswithcode.com/paper/rethinking-on-multi-stage-networks-for-human | Rethinking on Multi-Stage Networks for Human Pose Estimation | 1901.00148 | https://arxiv.org/abs/1901.00148v4 | https://arxiv.org/pdf/1901.00148v4.pdf | https://github.com/chenyilun95/tf-cpn | false | false | true | tf |
https://paperswithcode.com/paper/semantic-visual-navigation-by-watching | Semantic Visual Navigation by Watching YouTube Videos | 2006.10034 | https://arxiv.org/abs/2006.10034v2 | https://arxiv.org/pdf/2006.10034v2.pdf | https://github.com/MatthewChang/video-dqn | true | false | false | pytorch |
https://paperswithcode.com/paper/sound-event-detection-and-time-frequency | Sound Event Detection and Time-Frequency Segmentation from Weakly Labelled Data | 1804.04715 | http://arxiv.org/abs/1804.04715v2 | http://arxiv.org/pdf/1804.04715v2.pdf | https://github.com/qiuqiangkong/sed_time_freq_segmentation | true | true | false | pytorch |
https://paperswithcode.com/paper/dueling-network-architectures-for-deep | Dueling Network Architectures for Deep Reinforcement Learning | 1511.06581 | http://arxiv.org/abs/1511.06581v3 | http://arxiv.org/pdf/1511.06581v3.pdf | https://github.com/prajwalgatti/DRL-Continuous-Control | false | false | true | none |
https://paperswithcode.com/paper/very-deep-convolutional-networks-for-large | Very Deep Convolutional Networks for Large-Scale Image Recognition | 1409.1556 | http://arxiv.org/abs/1409.1556v6 | http://arxiv.org/pdf/1409.1556v6.pdf | https://github.com/Tools4Project/4501Project | false | false | true | tf |
https://paperswithcode.com/paper/efficient-training-of-energy-based-models-via | Efficient training of energy-based models via spin-glass control | 1910.01592 | https://arxiv.org/abs/1910.01592v4 | https://arxiv.org/pdf/1910.01592v4.pdf | https://github.com/apozas/rapid | true | true | true | pytorch |
https://paperswithcode.com/paper/context-dependent-fine-grained-entity-type | Context-Dependent Fine-Grained Entity Type Tagging | 1412.1820 | http://arxiv.org/abs/1412.1820v2 | http://arxiv.org/pdf/1412.1820v2.pdf | https://github.com/shanzhenren/AFET | false | false | true | none |
https://paperswithcode.com/paper/sampling-generative-networks | Sampling Generative Networks | 1609.04468 | http://arxiv.org/abs/1609.04468v3 | http://arxiv.org/pdf/1609.04468v3.pdf | https://github.com/ptrblck/prog_gans_pytorch_inference | false | false | true | pytorch |
https://paperswithcode.com/paper/breaking-the-curse-of-space-explosion-towards | Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search | 2007.07197 | https://arxiv.org/abs/2007.07197v2 | https://arxiv.org/pdf/2007.07197v2.pdf | https://github.com/guoyongcs/CNAS | true | true | false | pytorch |
https://paperswithcode.com/paper/learning-imbalanced-datasets-with-label | Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss | 1906.07413 | https://arxiv.org/abs/1906.07413v2 | https://arxiv.org/pdf/1906.07413v2.pdf | https://github.com/feidfoe/AdjustBnd4Imbalance | false | false | true | pytorch |
https://paperswithcode.com/paper/learning-2d-temporal-adjacent-networks-for | Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language | 1912.03590 | https://arxiv.org/abs/1912.03590v3 | https://arxiv.org/pdf/1912.03590v3.pdf | https://github.com/researchmm/2D-TAN | false | false | true | pytorch |
https://paperswithcode.com/paper/multigrid-predictive-filter-flow-for | Multigrid Predictive Filter Flow for Unsupervised Learning on Videos | 1904.01693 | http://arxiv.org/abs/1904.01693v1 | http://arxiv.org/pdf/1904.01693v1.pdf | https://github.com/bestaar/predictiveFilterFlow | false | false | true | pytorch |
https://paperswithcode.com/paper/image-reconstruction-with-predictive-filter | Image Reconstruction with Predictive Filter Flow | 1811.11482 | http://arxiv.org/abs/1811.11482v1 | http://arxiv.org/pdf/1811.11482v1.pdf | https://github.com/bestaar/predictiveFilterFlow | false | false | true | pytorch |
https://paperswithcode.com/paper/revisiting-unreasonable-effectiveness-of-data | Revisiting Unreasonable Effectiveness of Data in Deep Learning Era | 1707.02968 | http://arxiv.org/abs/1707.02968v2 | http://arxiv.org/pdf/1707.02968v2.pdf | https://github.com/Tencent/tencent-ml-images | false | false | true | tf |
https://paperswithcode.com/paper/general-purpose-atomic-crosschain | General Purpose Atomic Crosschain Transactions | 2011.12783 | https://arxiv.org/abs/2011.12783v4 | https://arxiv.org/pdf/2011.12783v4.pdf | https://github.com/ConsenSys/gpact | true | true | true | none |
https://paperswithcode.com/paper/ms-dpps-multi-source-determinantal-point | MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image Retrieval | 2507.06654 | https://arxiv.org/abs/2507.06654v1 | https://arxiv.org/pdf/2507.06654v1.pdf | https://github.com/nec-n-sogi/msdpp | true | true | true | pytorch |
https://paperswithcode.com/paper/atlas-end-to-end-3d-scene-reconstruction-from | Atlas: End-to-End 3D Scene Reconstruction from Posed Images | 2003.10432 | https://arxiv.org/abs/2003.10432v3 | https://arxiv.org/pdf/2003.10432v3.pdf | https://github.com/magicleap/Atlas | false | false | true | pytorch |
https://paperswithcode.com/paper/nimbro-op2x-adult-sized-open-source-3d | NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot | 1810.08395 | http://arxiv.org/abs/1810.08395v1 | http://arxiv.org/pdf/1810.08395v1.pdf | https://github.com/iswariyam/Mini-semantic-segmentation-network-pytorch | false | false | true | pytorch |
https://paperswithcode.com/paper/invariance-analysis-of-saliency-models-versus | Invariance Analysis of Saliency Models versus Human Gaze During Scene Free Viewing | 1810.04456 | http://arxiv.org/abs/1810.04456v1 | http://arxiv.org/pdf/1810.04456v1.pdf | https://github.com/CZHQuality/Sal-CFS-GAN | false | false | true | tf |
https://paperswithcode.com/paper/unpaired-image-to-image-translation-using | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks | 1703.10593 | https://arxiv.org/abs/1703.10593v7 | https://arxiv.org/pdf/1703.10593v7.pdf | https://github.com/Shumway82/CycleGAN | false | false | true | tf |
https://paperswithcode.com/paper/semi-supervised-learning-with-ladder-networks | Semi-Supervised Learning with Ladder Networks | 1507.02672 | http://arxiv.org/abs/1507.02672v2 | http://arxiv.org/pdf/1507.02672v2.pdf | https://github.com/CuriousAI/ladder | false | false | true | none |
https://paperswithcode.com/paper/bayesian-optimization-of-hyper-parameters-in | Bayesian optimization of hyper-parameters in reservoir computing | 1611.05193 | http://arxiv.org/abs/1611.05193v3 | http://arxiv.org/pdf/1611.05193v3.pdf | https://github.com/rednotion/parallel_esn_web | false | false | true | none |
https://paperswithcode.com/paper/convolutional-neural-network-architecture-for | Convolutional neural network architecture for geometric matching | 1703.05593 | http://arxiv.org/abs/1703.05593v2 | http://arxiv.org/pdf/1703.05593v2.pdf | https://github.com/ignacio-rocco/cnngeometric_pytorch | false | false | true | pytorch |
https://paperswithcode.com/paper/neural-audio-synthesis-of-musical-notes-with | Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders | 1704.01279 | http://arxiv.org/abs/1704.01279v1 | http://arxiv.org/pdf/1704.01279v1.pdf | https://github.com/NoaCahan/WavenetAutoEncoder | false | false | true | pytorch |
https://paperswithcode.com/paper/tips-and-tricks-for-visual-question-answering | Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge | 1708.02711 | http://arxiv.org/abs/1708.02711v1 | http://arxiv.org/pdf/1708.02711v1.pdf | https://github.com/feifengwhu/question_attention | false | false | true | pytorch |
https://paperswithcode.com/paper/retinal-vessel-segmentation-based-on-fully | Retinal vessel segmentation based on Fully Convolutional Neural Networks | 1812.07110 | http://arxiv.org/abs/1812.07110v2 | http://arxiv.org/pdf/1812.07110v2.pdf | https://github.com/americofmoliveira/VesselSegmentation_ESWA | false | false | true | none |
https://paperswithcode.com/paper/randomized-matrix-decompositions-using-r | Randomized Matrix Decompositions using R | 1608.02148 | http://arxiv.org/abs/1608.02148v4 | http://arxiv.org/pdf/1608.02148v4.pdf | https://github.com/Benli11/ristretto | false | false | true | none |
https://paperswithcode.com/paper/modified-shallow-water-equations-for | Modified Shallow Water Equations for significantly varying seabeds | 1202.6542 | http://arxiv.org/abs/1202.6542v6 | http://arxiv.org/pdf/1202.6542v6.pdf | https://github.com/huwb/crest-oceanrender | false | false | true | none |
https://paperswithcode.com/paper/semantic-document-distance-measures-and | Semantic Document Distance Measures and Unsupervised Document Revision Detection | 1709.01256 | http://arxiv.org/abs/1709.01256v2 | http://arxiv.org/pdf/1709.01256v2.pdf | https://github.com/XiaofengZhu/wDTW-wTED | true | true | true | none |
https://paperswithcode.com/paper/revisiting-decomposable-submodular-function | Revisiting Decomposable Submodular Function Minimization with Incidence Relations | 1803.03851 | http://arxiv.org/abs/1803.03851v3 | http://arxiv.org/pdf/1803.03851v3.pdf | https://github.com/lipan00123/DSFM-with-incidence-relations | true | true | false | none |
https://paperswithcode.com/paper/learning-deep-representations-of-fine-grained | Learning Deep Representations of Fine-grained Visual Descriptions | 1605.05395 | http://arxiv.org/abs/1605.05395v1 | http://arxiv.org/pdf/1605.05395v1.pdf | https://github.com/Maymaher/StackGANv2 | false | false | true | pytorch |
https://paperswithcode.com/paper/an-end-to-end-trainable-neural-network-for | An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition | 1507.05717 | http://arxiv.org/abs/1507.05717v1 | http://arxiv.org/pdf/1507.05717v1.pdf | https://github.com/bai-shang/crnn_ctc_ocr.Tensorflow | false | false | true | tf |
https://paperswithcode.com/paper/learning-to-learn-without-forgetting-by | Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference | 1810.11910 | https://arxiv.org/abs/1810.11910v3 | https://arxiv.org/pdf/1810.11910v3.pdf | https://github.com/mattriemer/mer | true | true | false | pytorch |
https://paperswithcode.com/paper/pythia-v01-the-winning-entry-to-the-vqa | Pythia v0.1: the Winning Entry to the VQA Challenge 2018 | 1807.09956 | http://arxiv.org/abs/1807.09956v2 | http://arxiv.org/pdf/1807.09956v2.pdf | https://github.com/songhe17/pythia-clone | false | false | true | pytorch |
https://paperswithcode.com/paper/generative-adversarial-text-to-image | Generative Adversarial Text to Image Synthesis | 1605.05396 | http://arxiv.org/abs/1605.05396v2 | http://arxiv.org/pdf/1605.05396v2.pdf | https://github.com/Maymaher/StackGANv2 | false | false | true | pytorch |
https://paperswithcode.com/paper/attngan-fine-grained-text-to-image-generation | AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks | 1711.10485 | http://arxiv.org/abs/1711.10485v1 | http://arxiv.org/pdf/1711.10485v1.pdf | https://github.com/Maymaher/StackGANv2 | false | false | true | pytorch |
https://paperswithcode.com/paper/benchmarking-machine-learning-models-on-eicu | Benchmarking machine learning models on multi-centre eICU critical care dataset | 1910.00964 | https://arxiv.org/abs/1910.00964v3 | https://arxiv.org/pdf/1910.00964v3.pdf | https://github.com/mostafaalishahi/eICU_Benchmark | true | true | true | none |
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