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

https://github.com/minhnhat93/tf-SNDCGAN

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

https://github.com/minostauros/VIAR

Unsupervised Learning of View-invariant Action Representations

https://github.com/minyoungg/wmigftl

What makes ImageNet good for transfer learning?

https://github.com/mira-space/mira

MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions

https://github.com/mira-space/miradata

MiraData: A Large-Scale Video Dataset with Long Durations and Structured Captions

https://github.com/mirthai/csa-net

Non-local Neural Networks

https://github.com/mitya8128/experiments_notes

Graph Attention Networks

https://github.com/mjhucla/TF-mRNN

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mjhucla/mRNN-CR

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mjoannou/audiovisual-moments-in-time

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mkang315/rcs-yolo

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/mkimhi/RISE

In Defense of Online Models for Video Instance Segmentation

https://github.com/ml-gsai/concat-id

Concat-ID: Towards Universal Identity-Preserving Video Synthesis

https://github.com/mlakhal/gtnet

Novel-View Human Action Synthesis

https://github.com/mlfoundations/open_clip

CoCa: Contrastive Captioners are Image-Text Foundation Models

https://github.com/mlfoundations/open_flamingo

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/mlzxzhou/keras-gnm

Graph Attention Networks

https://github.com/mnecipkurt/tsg19

Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach

https://github.com/mnikitin/ECANet

Gated Channel Transformation for Visual Recognition

https://github.com/mnikitin/channel-attention

Gated Channel Transformation for Visual Recognition

https://github.com/moabitcoin/ig65m-pytorch

The Kinetics Human Action Video Dataset

https://github.com/modadundun/ProDisc-VAD

ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications

https://github.com/modelhub-ai/vgg-19

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/modestyachts/ImageNetV2

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mogadmd/YT8M

Learnable pooling with Context Gating for video classification

https://github.com/mohamedameen93/German-Traffic-Sign-Classification-Using-TensorFlow

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mohsenzand/reschunk

Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction

https://github.com/motional/nuplan-devkit

NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

https://github.com/mpuu00001/Siformer

Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition

https://github.com/mr-bulb/DL_basic_github

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mryoo/pooled_time_series

Pooled Motion Features for First-Person Videos

https://github.com/mseitzer/pytorch-fid

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

https://github.com/mshilpaa/DenseDepth

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/mtlouie-unm/alome-tsn

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

https://github.com/mtorabirad/PricePrediction

XGBoost: A Scalable Tree Boosting System

https://github.com/mukesh40744/Behavioral-Cloning

Learning a Driving Simulator

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