Datasets:
pwc-archive
/
links-between-paper-and-code

Dataset card Data Studio Files
xet
Community
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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-Keras
false
false
true
tf
https://paperswithcode.com/paper/accurate-3d-localization-for-mav-swarms-by
Accurate 3D Localization for MAV Swarms by UWB and IMU Fusion
1807.10913
http://arxiv.org/abs/1807.10913v1
http://arxiv.org/pdf/1807.10913v1.pdf
https://github.com/lijx10/uwb-localization
true
true
true
none
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/titu1994/Super-Resolution-using-Generative-Adversarial-Networks
false
false
true
none
https://paperswithcode.com/paper/structural-learning-of-probabilistic
Structural Learning of Probabilistic Sentential Decision Diagrams under Partial Closed-World Assumption
2107.12130
https://arxiv.org/abs/2107.12130v1
https://arxiv.org/pdf/2107.12130v1.pdf
https://github.com/IDSIA-papers/2021-TPM
true
true
false
none
https://paperswithcode.com/paper/classifying-conversation-in-digital
Classifying Conversation in Digital Communication
1801.10527
http://arxiv.org/abs/1801.10527v1
http://arxiv.org/pdf/1801.10527v1.pdf
https://github.com/empiricalstateofmind/eventgraphs
true
false
true
none
https://paperswithcode.com/paper/real-time-2d-multi-person-pose-estimation-on
Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose
1811.12004
http://arxiv.org/abs/1811.12004v1
http://arxiv.org/pdf/1811.12004v1.pdf
https://github.com/murdockhou/lightweight_openpose
false
false
true
tf
https://paperswithcode.com/paper/virtual-cnn-branching-efficient-feature
Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification
1803.05872
http://arxiv.org/abs/1803.05872v1
http://arxiv.org/pdf/1803.05872v1.pdf
https://github.com/agongt408/vbranch
false
false
true
tf
https://paperswithcode.com/paper/in-defense-of-the-triplet-loss-for-person-re
In Defense of the Triplet Loss for Person Re-Identification
1703.07737
http://arxiv.org/abs/1703.07737v4
http://arxiv.org/pdf/1703.07737v4.pdf
https://github.com/agongt408/vbranch
false
false
true
tf
https://paperswithcode.com/paper/pristi-a-conditional-diffusion-framework-for
PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation
2302.09746
https://arxiv.org/abs/2302.09746v1
https://arxiv.org/pdf/2302.09746v1.pdf
https://github.com/lmzzml/pristi
true
true
false
pytorch
https://paperswithcode.com/paper/mixed-effect-composite-rnn-gp-a-personalized
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare
1806.01551
https://arxiv.org/abs/1806.01551v3
https://arxiv.org/pdf/1806.01551v3.pdf
https://github.com/OpenXAIProject/Mixed-Effect-Composite-RNN-Gaussian-Process
false
false
true
tf
https://paperswithcode.com/paper/adversarial-attacks-on-time-series
Adversarial Attacks on Time Series
1902.10755
http://arxiv.org/abs/1902.10755v2
http://arxiv.org/pdf/1902.10755v2.pdf
https://github.com/titu1994/Adversarial-Attacks-Time-Series
false
false
true
tf
https://paperswithcode.com/paper/prototypical-representation-learning-for-1
Prototypical Representation Learning for Relation Extraction
2103.11647
https://arxiv.org/abs/2103.11647v1
https://arxiv.org/pdf/2103.11647v1.pdf
https://github.com/Alibaba-NLP/ProtoRE
true
true
false
pytorch
https://paperswithcode.com/paper/sparse-multiway-decomposition-for-analysis
Sparse multiway decomposition for analysis and modeling of diffusion imaging and tractography
1505.07170
http://arxiv.org/abs/1505.07170v1
http://arxiv.org/pdf/1505.07170v1.pdf
https://github.com/brainlife/app-life
false
false
true
none
https://paperswithcode.com/paper/attanet-attention-augmented-network-for-fast
AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing
2103.05930
https://arxiv.org/abs/2103.05930v1
https://arxiv.org/pdf/2103.05930v1.pdf
https://github.com/songqi-github/AttaNet
false
false
true
pytorch
https://paperswithcode.com/paper/ssd-single-shot-multibox-detector
SSD: Single Shot MultiBox Detector
1512.02325
http://arxiv.org/abs/1512.02325v5
http://arxiv.org/pdf/1512.02325v5.pdf
https://github.com/nsom/ssd
false
false
true
pytorch
https://paperswithcode.com/paper/dissent-sentence-representation-learning-from
DisSent: Sentence Representation Learning from Explicit Discourse Relations
1710.04334
https://arxiv.org/abs/1710.04334v4
https://arxiv.org/pdf/1710.04334v4.pdf
https://github.com/facebookresearch/InferSent
false
false
true
pytorch
https://paperswithcode.com/paper/pixel-wise-attentional-gating-for
Pixel-wise Attentional Gating for Parsimonious Pixel Labeling
1805.01556
http://arxiv.org/abs/1805.01556v2
http://arxiv.org/pdf/1805.01556v2.pdf
https://github.com/aimerykong/Pixel-Attentional-Gating
true
true
true
none
https://paperswithcode.com/paper/chexnet-radiologist-level-pneumonia-detection
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
1711.05225
http://arxiv.org/abs/1711.05225v3
http://arxiv.org/pdf/1711.05225v3.pdf
https://github.com/Azure/AzureChestXRay
false
false
true
pytorch
https://paperswithcode.com/paper/random-erasing-data-augmentation
Random Erasing Data Augmentation
1708.04896
http://arxiv.org/abs/1708.04896v2
http://arxiv.org/pdf/1708.04896v2.pdf
https://github.com/NVlabs/DG-Net
false
false
false
pytorch
https://paperswithcode.com/paper/mnemonic-descent-method-a-recurrent-process-1
Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment
null
https://www.ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf
https://www.ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf
https://github.com/trigeorgis/mdm
false
false
false
tf
https://paperswithcode.com/paper/catboost-unbiased-boosting-with-categorical
CatBoost: unbiased boosting with categorical features
1706.09516
http://arxiv.org/abs/1706.09516v5
http://arxiv.org/pdf/1706.09516v5.pdf
https://github.com/yumoh/catboost_iter
false
false
true
none
https://paperswithcode.com/paper/unity-a-general-platform-for-intelligent
Unity: A General Platform for Intelligent Agents
1809.02627
https://arxiv.org/abs/1809.02627v2
https://arxiv.org/pdf/1809.02627v2.pdf
https://github.com/Henreich/ML-Pong
false
false
true
tf
https://paperswithcode.com/paper/if-you-like-it-gan-it-probabilistic
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN
2005.01181
https://arxiv.org/abs/2005.01181v1
https://arxiv.org/pdf/2005.01181v1.pdf
https://github.com/flaviagiammarino/probcast-tensorflow
false
false
true
tf
https://paperswithcode.com/paper/reading-between-the-lines-can-llms-identify
Reading between the Lines: Can LLMs Identify Cross-Cultural Communication Gaps?
2502.09636
https://arxiv.org/abs/2502.09636v2
https://arxiv.org/pdf/2502.09636v2.pdf
https://github.com/sougata-ub/reading_between_lines
true
true
false
none
https://paperswithcode.com/paper/semi-supervised-learning-with-deep-generative-1
Semi-Supervised Learning with Deep Generative Models
1406.5298
http://arxiv.org/abs/1406.5298v2
http://arxiv.org/pdf/1406.5298v2.pdf
https://github.com/enalisnick/stick-breaking_dgms
false
false
true
none
https://paperswithcode.com/paper/generalised-dice-overlap-as-a-deep-learning
Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
1707.03237
http://arxiv.org/abs/1707.03237v3
http://arxiv.org/pdf/1707.03237v3.pdf
https://github.com/neshitov/UNet
false
false
true
pytorch
https://paperswithcode.com/paper/crowdsourcing-gaze-data-collection
Crowdsourcing Gaze Data Collection
1204.3367
https://arxiv.org/abs/1204.3367v1
https://arxiv.org/pdf/1204.3367v1.pdf
https://github.com/turkeyes/codecharts
false
false
true
none
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/trongnghia00/darknet
false
false
true
none
https://paperswithcode.com/paper/single-shot-refinement-neural-network-for
Single-Shot Refinement Neural Network for Object Detection
1711.06897
http://arxiv.org/abs/1711.06897v3
http://arxiv.org/pdf/1711.06897v3.pdf
https://github.com/laycoding/FaceDetection
false
false
true
none
https://paperswithcode.com/paper/pylearn2-a-machine-learning-research-library
Pylearn2: a machine learning research library
1308.4214
http://arxiv.org/abs/1308.4214v1
http://arxiv.org/pdf/1308.4214v1.pdf
https://github.com/jacobpeplinskiV2/pylearn2
false
false
true
none
https://paperswithcode.com/paper/which-encoding-is-the-best-for-text
Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean?
1708.02657
http://arxiv.org/abs/1708.02657v2
http://arxiv.org/pdf/1708.02657v2.pdf
https://github.com/zhangxiangxiao/glyph
false
false
true
none
https://paperswithcode.com/paper/fooling-lidar-perception-via-adversarial
Fooling LiDAR Perception via Adversarial Trajectory Perturbation
2103.15326
https://arxiv.org/abs/2103.15326v2
https://arxiv.org/pdf/2103.15326v2.pdf
https://github.com/ai4ce/FLAT
false
false
true
pytorch
https://paperswithcode.com/paper/density-estimation-using-real-nvp
Density estimation using Real NVP
1605.08803
http://arxiv.org/abs/1605.08803v3
http://arxiv.org/pdf/1605.08803v3.pdf
https://github.com/ANLGBOY/RealNVP-with-PyTorch
false
false
true
pytorch
https://paperswithcode.com/paper/graph-to-sequence-learning-using-gated-graph
Graph-to-Sequence Learning using Gated Graph Neural Networks
1806.09835
http://arxiv.org/abs/1806.09835v1
http://arxiv.org/pdf/1806.09835v1.pdf
https://github.com/beckdaniel/acl2018_graph2seq
true
true
false
mxnet
https://paperswithcode.com/paper/robust-probabilistic-modeling-with-bayesian
Robust Probabilistic Modeling with Bayesian Data Reweighting
1606.03860
http://arxiv.org/abs/1606.03860v3
http://arxiv.org/pdf/1606.03860v3.pdf
https://github.com/yixinwang/robust-rpm-public
false
false
true
none
https://paperswithcode.com/paper/fine-grained-analysis-of-sentence-embeddings
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks
1608.04207
http://arxiv.org/abs/1608.04207v3
http://arxiv.org/pdf/1608.04207v3.pdf
https://github.com/facebookresearch/InferSent
false
false
true
pytorch
https://paperswithcode.com/paper/a-human-computer-interface-design-for
A Human-Computer Interface Design for Quantitative Measure of Regret Theory
1810.00462
http://arxiv.org/abs/1810.00462v1
http://arxiv.org/pdf/1810.00462v1.pdf
https://github.com/I2RLab/RegretMeasurement-GUI
false
false
true
none
https://paperswithcode.com/paper/asynchronous-methods-for-deep-reinforcement
Asynchronous Methods for Deep Reinforcement Learning
1602.01783
http://arxiv.org/abs/1602.01783v2
http://arxiv.org/pdf/1602.01783v2.pdf
https://github.com/ShibiHe/Q-Optimality-Tightening
false
false
true
none
https://paperswithcode.com/paper/facenet-a-unified-embedding-for-face
FaceNet: A Unified Embedding for Face Recognition and Clustering
1503.03832
http://arxiv.org/abs/1503.03832v3
http://arxiv.org/pdf/1503.03832v3.pdf
https://github.com/CharlesPikachu/CharlesFace
false
false
true
pytorch
https://paperswithcode.com/paper/playgol-learning-programs-through-play
Playgol: learning programs through play
1904.08993
https://arxiv.org/abs/1904.08993v2
https://arxiv.org/pdf/1904.08993v2.pdf
https://github.com/metagol/metagol
true
true
false
none
https://paperswithcode.com/paper/richer-convolutional-features-for-edge
Richer Convolutional Features for Edge Detection
1612.02103
https://arxiv.org/abs/1612.02103v3
https://arxiv.org/pdf/1612.02103v3.pdf
https://github.com/meteorshowers/RCF-pytorch
false
false
false
pytorch
https://paperswithcode.com/paper/first-steps-toward-camera-model
First Steps Toward Camera Model Identification with Convolutional Neural Networks
1603.01068
http://arxiv.org/abs/1603.01068v2
http://arxiv.org/pdf/1603.01068v2.pdf
https://github.com/polimi-ispl/camera-model-identification-with-cnn
false
false
false
caffe2
https://paperswithcode.com/paper/deriving-machine-attention-from-human
Deriving Machine Attention from Human Rationales
1808.09367
http://arxiv.org/abs/1808.09367v1
http://arxiv.org/pdf/1808.09367v1.pdf
https://github.com/Sein-Jang/Deriving-Machine-Attention-from-Human-Rationales
false
false
true
pytorch
https://paperswithcode.com/paper/convex-pentagons-that-admit-i-block
Convex pentagons that admit $i$-block transitive tilings
1510.01186
http://arxiv.org/abs/1510.01186v1
http://arxiv.org/pdf/1510.01186v1.pdf
https://github.com/justinjk007/Pentagonal-tiling
false
false
true
none
https://paperswithcode.com/paper/learning-to-see-in-the-dark
Learning to See in the Dark
1805.01934
http://arxiv.org/abs/1805.01934v1
http://arxiv.org/pdf/1805.01934v1.pdf
https://github.com/cydonia999/Learning_to_See_in_the_Dark_PyTorch
false
false
true
pytorch
https://paperswithcode.com/paper/improved-adversarial-systems-for-3d-object
Improved Adversarial Systems for 3D Object Generation and Reconstruction
1707.09557
http://arxiv.org/abs/1707.09557v3
http://arxiv.org/pdf/1707.09557v3.pdf
https://github.com/kingcheng2000/3D-IWGAN
false
false
true
none
https://paperswithcode.com/paper/uncertainty-aware-joint-salient-object-and
Uncertainty-aware Joint Salient Object and Camouflaged Object Detection
2104.02628
https://arxiv.org/abs/2104.02628v1
https://arxiv.org/pdf/2104.02628v1.pdf
https://github.com/JingZhang617/Joint_COD_SOD
true
true
false
pytorch
https://paperswithcode.com/paper/brain-tumor-segmentation-with-deep-neural
Brain Tumor Segmentation with Deep Neural Networks
1505.03540
http://arxiv.org/abs/1505.03540v3
http://arxiv.org/pdf/1505.03540v3.pdf
https://github.com/IAmSuyogJadhav/Brainy
false
false
true
none
https://paperswithcode.com/paper/texygen-a-benchmarking-platform-for-text
Texygen: A Benchmarking Platform for Text Generation Models
1802.01886
http://arxiv.org/abs/1802.01886v1
http://arxiv.org/pdf/1802.01886v1.pdf
https://github.com/geek-ai/Texygen
true
true
true
tf
https://paperswithcode.com/paper/compar-optimized-multi-compiler-for-automatic
ComPar: Optimized Multi-Compiler for Automatic OpenMP S2S Parallelization
2005.13304
http://arxiv.org/abs/2005.13304v1
http://arxiv.org/pdf/2005.13304v1.pdf
https://github.com/Scientific-Computing-Lab-NRCN/compar
true
true
false
none
https://paperswithcode.com/paper/asynchronous-bidirectional-decoding-for
Asynchronous Bidirectional Decoding for Neural Machine Translation
1801.05122
http://arxiv.org/abs/1801.05122v2
http://arxiv.org/pdf/1801.05122v2.pdf
https://github.com/DeepLearnXMU/ABD-NMT
true
true
false
none
https://paperswithcode.com/paper/vr-sgd-a-simple-stochastic-variance-reduction
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning
1802.09932
http://arxiv.org/abs/1802.09932v2
http://arxiv.org/pdf/1802.09932v2.pdf
https://github.com/jnhujnhu/VR-SGD
true
true
false
none
https://paperswithcode.com/paper/neural-machine-translation
Neural Machine Translation
1709.07809
http://arxiv.org/abs/1709.07809v1
http://arxiv.org/pdf/1709.07809v1.pdf
https://github.com/neulab/xnmt
true
true
false
none
https://paperswithcode.com/paper/stacked-attention-networks-for-image-question
Stacked Attention Networks for Image Question Answering
1511.02274
http://arxiv.org/abs/1511.02274v2
http://arxiv.org/pdf/1511.02274v2.pdf
https://github.com/zcyang/imageqa-san
false
false
true
none
https://paperswithcode.com/paper/playing-hard-exploration-games-by-watching
Playing hard exploration games by watching YouTube
1805.11592
http://arxiv.org/abs/1805.11592v2
http://arxiv.org/pdf/1805.11592v2.pdf
https://github.com/MaxSobolMark/HardRLWithYoutube
false
false
true
tf
https://paperswithcode.com/paper/task-agnostic-continual-learning-using-online
Task Agnostic Continual Learning Using Online Variational Bayes
1803.10123
http://arxiv.org/abs/1803.10123v3
http://arxiv.org/pdf/1803.10123v3.pdf
https://github.com/taldatech/tf-bgd
false
false
true
tf
https://paperswithcode.com/paper/on-the-design-of-deep-priors-for-unsupervised
On the Design of Deep Priors for Unsupervised Audio Restoration
2104.07161
https://arxiv.org/abs/2104.07161v1
https://arxiv.org/pdf/2104.07161v1.pdf
https://github.com/vivsivaraman/designaudiopriors
true
true
false
pytorch
https://paperswithcode.com/paper/decoding-the-style-and-bias-of-song-lyrics
Decoding the Style and Bias of Song Lyrics
1907.07818
https://arxiv.org/abs/1907.07818v1
https://arxiv.org/pdf/1907.07818v1.pdf
https://github.com/manashpratim/Decoding-the-Style-and-Bias-of-Song-Lyrics
true
true
false
none
https://paperswithcode.com/paper/improving-the-resolution-of-cnn-feature-maps
Improving the Resolution of CNN Feature Maps Efficiently with Multisampling
1805.10766
https://arxiv.org/abs/1805.10766v2
https://arxiv.org/pdf/1805.10766v2.pdf
https://github.com/ShayanPersonal/checkered-cnn
true
true
true
pytorch
https://paperswithcode.com/paper/dual-gaussian-based-variational-subspace
Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification
2008.02520
https://arxiv.org/abs/2008.02520v1
https://arxiv.org/pdf/2008.02520v1.pdf
https://github.com/TPCD/DG-VAE
true
false
true
pytorch
https://paperswithcode.com/paper/multi-level-visual-similarity-based
Multi-Level Visual Similarity Based Personalized Tourist Attraction Recommendation Using Geo-Tagged Photos
2109.08275
https://arxiv.org/abs/2109.08275v2
https://arxiv.org/pdf/2109.08275v2.pdf
https://github.com/revaludo/MEAL
true
true
false
tf
https://paperswithcode.com/paper/efficient-learning-for-deep-quantum-neural
Efficient Learning for Deep Quantum Neural Networks
1902.10445
http://arxiv.org/abs/1902.10445v1
http://arxiv.org/pdf/1902.10445v1.pdf
https://github.com/R8monaW/DeepQNN
true
true
false
none
https://paperswithcode.com/paper/point-classification-with-runge-kutta
Classification with Runge-Kutta networks and feature space augmentation
2104.02369
https://arxiv.org/abs/2104.02369v2
https://arxiv.org/pdf/2104.02369v2.pdf
https://github.com/ElisaGiesecke/augmented-RK-Nets
true
true
false
pytorch
https://paperswithcode.com/paper/coreference-resolution-with-entity
Coreference Resolution with Entity Equalization
null
https://aclanthology.org/P19-1066
https://aclanthology.org/P19-1066.pdf
https://github.com/kkjawz/coref-ee
false
true
false
tf
https://paperswithcode.com/paper/necola-towards-a-universal-field-level
NECOLA: Towards a Universal Field-level Cosmological Emulator
2111.02441
https://arxiv.org/abs/2111.02441v1
https://arxiv.org/pdf/2111.02441v1.pdf
https://github.com/HAWinther/MG-PICOLA-PUBLIC
true
true
false
none
https://paperswithcode.com/paper/perceptual-quality-assessment-of-smartphone
Perceptual Quality Assessment of Smartphone Photography
null
http://openaccess.thecvf.com/content_CVPR_2020/html/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.html
http://openaccess.thecvf.com/content_CVPR_2020/papers/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.pdf
https://github.com/h4nwei/SPAQ
true
true
false
pytorch
https://paperswithcode.com/paper/llmjudge-llms-for-relevance-judgments
LLMJudge: LLMs for Relevance Judgments
2408.08896
https://arxiv.org/abs/2408.08896v1
https://arxiv.org/pdf/2408.08896v1.pdf
https://github.com/llm4eval/LLMJudge
true
false
false
none
https://paperswithcode.com/paper/mebow-monocular-estimation-of-body-1
MEBOW: Monocular Estimation of Body Orientation In the Wild
2011.13688
https://arxiv.org/abs/2011.13688v1
https://arxiv.org/pdf/2011.13688v1.pdf
https://github.com/ChenyanWu/MEBOW
false
false
false
pytorch
https://paperswithcode.com/paper/cr-gan-learning-complete-representations-for
CR-GAN: Learning Complete Representations for Multi-view Generation
1806.11191
http://arxiv.org/abs/1806.11191v1
http://arxiv.org/pdf/1806.11191v1.pdf
https://github.com/bluer555/CR-GAN
true
true
true
pytorch
https://paperswithcode.com/paper/sparse-classification-and-phase-transitions-a
Sparse Classification and Phase Transitions: A Discrete Optimization Perspective
1710.01352
http://arxiv.org/abs/1710.01352v1
http://arxiv.org/pdf/1710.01352v1.pdf
https://github.com/jeanpauphilet/SubsetSelectionCIO.jl
false
false
true
none
https://paperswithcode.com/paper/variational-dropout-sparsifies-deep-neural
Variational Dropout Sparsifies Deep Neural Networks
1701.05369
http://arxiv.org/abs/1701.05369v3
http://arxiv.org/pdf/1701.05369v3.pdf
https://github.com/ars-ashuha/variational-dropout-sparsifies-dnn
false
false
true
tf
https://paperswithcode.com/paper/a-neural-algorithm-of-artistic-style
A Neural Algorithm of Artistic Style
1508.06576
http://arxiv.org/abs/1508.06576v2
http://arxiv.org/pdf/1508.06576v2.pdf
https://github.com/ChetanTayal138/NeuralStyleTransfer
false
false
true
tf
https://paperswithcode.com/paper/deep-image-homography-estimation
Deep Image Homography Estimation
1606.03798
http://arxiv.org/abs/1606.03798v1
http://arxiv.org/pdf/1606.03798v1.pdf
https://github.com/mazenmel/Deep-homography-estimation-Pytorch
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/vaisakh-shaj/DeepLearning
false
false
true
tf
https://paperswithcode.com/paper/keyframe-based-monocular-slam-design-survey
Keyframe-based monocular SLAM: design, survey, and future directions
1607.00470
http://arxiv.org/abs/1607.00470v2
http://arxiv.org/pdf/1607.00470v2.pdf
https://github.com/adioshun/gitBook_DeepSlam
false
false
true
none
https://paperswithcode.com/paper/fiesta-fast-incremental-euclidean-distance
FIESTA: Fast Incremental Euclidean Distance Fields for Online Motion Planning of Aerial Robots
1903.02144
http://arxiv.org/abs/1903.02144v1
http://arxiv.org/pdf/1903.02144v1.pdf
https://github.com/HKUST-Aerial-Robotics/FIESTA
false
false
true
none
https://paperswithcode.com/paper/variational-disentanglement-for-rare-event
Variational Disentanglement for Rare Event Modeling
2009.08541
https://arxiv.org/abs/2009.08541v5
https://arxiv.org/pdf/2009.08541v5.pdf
https://github.com/zidixiu/VIE
true
true
true
pytorch
https://paperswithcode.com/paper/lookahead-optimizer-k-steps-forward-1-step
Lookahead Optimizer: k steps forward, 1 step back
1907.08610
https://arxiv.org/abs/1907.08610v2
https://arxiv.org/pdf/1907.08610v2.pdf
https://github.com/alphadl/lookahead.pytorch
false
false
true
pytorch
https://paperswithcode.com/paper/new-insights-into-black-bodies
New insights into black bodies
1201.1809
https://arxiv.org/abs/1201.1809v2
https://arxiv.org/pdf/1201.1809v2.pdf
https://github.com/mikecokina/elisa
false
false
true
none
https://paperswithcode.com/paper/geometry-based-data-generation
Geometry-Based Data Generation
1802.04927
http://arxiv.org/abs/1802.04927v4
http://arxiv.org/pdf/1802.04927v4.pdf
https://github.com/KrishnaswamyLab/SUGAR
true
false
true
none
https://paperswithcode.com/paper/reconstructing-functions-and-estimating
Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with the Hubble Parameter and SNe Ia
1910.03636
https://arxiv.org/abs/1910.03636v5
https://arxiv.org/pdf/1910.03636v5.pdf
https://github.com/Guo-Jian-Wang/refann
true
true
false
pytorch
https://paperswithcode.com/paper/object-detectors-emerge-in-deep-scene-cnns
Object Detectors Emerge in Deep Scene CNNs
1412.6856
http://arxiv.org/abs/1412.6856v2
http://arxiv.org/pdf/1412.6856v2.pdf
https://github.com/JepsonWong/CNN_Visualization
false
false
true
none
https://paperswithcode.com/paper/deep-reinforcement-learning-with-double-q
Deep Reinforcement Learning with Double Q-learning
1509.06461
http://arxiv.org/abs/1509.06461v3
http://arxiv.org/pdf/1509.06461v3.pdf
https://github.com/wtingda/DeepRLBreakout
false
false
true
tf
https://paperswithcode.com/paper/prediction-intervals-split-normal-mixture
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles
2007.09670
https://arxiv.org/abs/2007.09670v1
https://arxiv.org/pdf/2007.09670v1.pdf
https://github.com/tarik/pi-snm-qde
true
true
true
pytorch
https://paperswithcode.com/paper/sequential-stratified-regeneration-mcmc-for
Sequential Stratified Regeneration: MCMC for Large State Spaces with an Application to Subgraph Count Estimation
2012.03879
https://arxiv.org/abs/2012.03879v3
https://arxiv.org/pdf/2012.03879v3.pdf
https://github.com/dccspeed/ripple
true
true
true
none
https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object
YOLOv4: Optimal Speed and Accuracy of Object Detection
2004.10934
https://arxiv.org/abs/2004.10934v1
https://arxiv.org/pdf/2004.10934v1.pdf
https://github.com/hhk7734/tensorflow-yolov4
false
false
false
tf
https://paperswithcode.com/paper/from-monte-carlo-to-las-vegas-improving
From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets
1711.08442
http://arxiv.org/abs/1711.08442v1
http://arxiv.org/pdf/1711.08442v1.pdf
https://github.com/PurdueMINDS/MCLV-RBM
true
true
false
pytorch
https://paperswithcode.com/paper/a-neural-conversational-model
A Neural Conversational Model
1506.05869
http://arxiv.org/abs/1506.05869v3
http://arxiv.org/pdf/1506.05869v3.pdf
https://github.com/hamil168/Chatbots
false
false
true
tf
https://paperswithcode.com/paper/quantile-propagation-for-wasserstein
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
1912.10200
https://arxiv.org/abs/1912.10200v3
https://arxiv.org/pdf/1912.10200v3.pdf
https://github.com/RuiZhang2016/Quantile-Propagation-for-Wasserstein-Approximate-Gaussian-Processes
true
true
true
none
https://paperswithcode.com/paper/linguistic-features-for-readability
Linguistic Features for Readability Assessment
2006.00377
https://arxiv.org/abs/2006.00377v1
https://arxiv.org/pdf/2006.00377v1.pdf
https://github.com/TovlyDeutsch/Linguistic-Features-for-Readability
true
true
true
pytorch
https://paperswithcode.com/paper/gain-missing-data-imputation-using-generative
GAIN: Missing Data Imputation using Generative Adversarial Nets
1806.02920
http://arxiv.org/abs/1806.02920v1
http://arxiv.org/pdf/1806.02920v1.pdf
https://github.com/dhanajitb/GAIN-Pytorch
false
false
false
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/pskrunner14/face-DCGAN
false
false
true
tf
https://paperswithcode.com/paper/seboost-boosting-stochastic-learning-using
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
1609.00629
http://arxiv.org/abs/1609.00629v1
http://arxiv.org/pdf/1609.00629v1.pdf
https://github.com/eladrich/seboost
true
true
false
torch
https://paperswithcode.com/paper/binarized-neural-networks-training-deep
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
1602.02830
http://arxiv.org/abs/1602.02830v3
http://arxiv.org/pdf/1602.02830v3.pdf
https://github.com/csyhhu/MetaQuant
false
false
true
pytorch
https://paperswithcode.com/paper/refer360-circ-a-referring-expression
Refer360$^\circ$: A Referring Expression Recognition Dataset in 360$^\circ$ Images
null
https://aclanthology.org/2020.acl-main.644
https://aclanthology.org/2020.acl-main.644.pdf
https://github.com/volkancirik/refer360
false
false
false
pytorch
https://paperswithcode.com/paper/improving-patch-based-scene-text-script
Improving patch-based scene text script identification with ensembles of conjoined networks
1602.07480
http://arxiv.org/abs/1602.07480v2
http://arxiv.org/pdf/1602.07480v2.pdf
https://github.com/lluisgomez/script_identification
true
true
false
none
https://paperswithcode.com/paper/ladn-local-adversarial-disentangling-network
LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
1904.11272
https://arxiv.org/abs/1904.11272v2
https://arxiv.org/pdf/1904.11272v2.pdf
https://github.com/wangguanzhi/LADN
false
false
true
pytorch
https://paperswithcode.com/paper/stackgan-text-to-photo-realistic-image
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
1612.03242
http://arxiv.org/abs/1612.03242v2
http://arxiv.org/pdf/1612.03242v2.pdf
https://github.com/dhirajpatnaik16297/IMG-TXT-Generative-Adversarial-Network
false
false
true
tf
https://paperswithcode.com/paper/atomnas-fine-grained-end-to-end-neural-1
AtomNAS: Fine-Grained End-to-End Neural Architecture Search
1912.09640
https://arxiv.org/abs/1912.09640v2
https://arxiv.org/pdf/1912.09640v2.pdf
https://github.com/meijieru/AtomNAS
true
true
true
pytorch
https://paperswithcode.com/paper/deepercut-a-deeper-stronger-and-faster-multi
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
1605.03170
http://arxiv.org/abs/1605.03170v3
http://arxiv.org/pdf/1605.03170v3.pdf
https://github.com/orkqueen/depplabseongil
false
false
true
tf