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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/koshian2/OctConv-TFKeras

Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

https://github.com/kozistr/gan-metrics

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

https://github.com/kreimanlab/HumanIntentionInferenceZeroShot

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kshitij1489/object-tracking

Simple Online and Realtime Tracking with a Deep Association Metric

https://github.com/ksm26/spatiotemporal-predictions

PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning

https://github.com/kthksgy/pytorch-template

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/ktr-hubrt/WSAL

Localizing Anomalies from Weakly-Labeled Videos

https://github.com/kuanzi/Yolov3_DeepSort

Simple Online and Realtime Tracking with a Deep Association Metric

https://github.com/kumgleb/Lyft_motion_prediction

One Thousand and One Hours: Self-driving Motion Prediction Dataset

https://github.com/kunimasa-kawasaki/chainer-prednet

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

https://github.com/kunimasa-kawasaki/keras-prednet

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

https://github.com/kunjan-mhaske/Image-super-resolution-model-using-4-layers-CNN-from-PyTorch

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/kurtisdavid/MetaOrthogonalization

Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

https://github.com/kusumikakd/EEGLearn

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

https://github.com/kuzinalx/pytorch_objectdetecttrack

Simple Online and Realtime Tracking

https://github.com/kwai-kolors/kolors

DragAnything: Motion Control for Anything using Entity Representation

https://github.com/kwantommy/breast-cancer-diagnosis

XGBoost: A Scalable Tree Boosting System

https://github.com/kwantommy/fgvc6-kaggle-cassava-classification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kyafuk/3d-mim

Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics

https://github.com/kylemath/DeepEEG

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

https://github.com/kylemin/S3D

Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification

https://github.com/kynkaat/improved-precision-and-recall-metric

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kyoukuntaro/FCSN_for_ChangeDetection_IGARSS2018

Fully Convolutional Siamese Networks for Change Detection

https://github.com/l0stpenguin/DenseDepthResNet

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/l13025816/PGCN

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/l3th4l/SuperRes

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/l5shi/OCTAVE-CONVOLUTION

Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

https://github.com/lab-midas/lapnet

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

https://github.com/labmlai/annotated_deep_learning_paper_implementations

Graph Attention Networks

https://github.com/lamia482/octconv-pytorch

Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

https://github.com/laowng/GISR

Deep Back-Projection Networks For Super-Resolution

https://github.com/large-trajectory-model/atm

CoTracker: It is Better to Track Together

https://github.com/latte488/smth-smth-v2

The "something something" video database for learning and evaluating visual common sense

https://github.com/lauradhatt/Interesting-Reads

Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos

https://github.com/ldjlammers/Hide-and-Seek-CS4240

Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization

https://github.com/leaderj1001/Billion-scale-semi-supervised-learning

Billion-scale semi-supervised learning for image classification

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.