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

https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/MrSyee/rnn_autoenc

Unsupervised Learning of Video Representations using LSTMs

https://github.com/NASA-NeMO-Net/NeMO-Net

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/NSR9/DenseDepth

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/NUAAXQ/MLCVNet

Compact Generalized Non-local Network

https://github.com/NVIDIA/UnsupervisedLandmarkLearning

Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos

https://github.com/NVIDIA/flownet2-pytorch

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

https://github.com/NVIDIA/semantic-segmentation/tree/sdcnet/sdcnet

https://github.com/NVIDIA/unsupervised-video-interpolation

Video Frame Synthesis using Deep Voxel Flow

https://github.com/NVlabs/eg3d

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

https://github.com/NVlabs/few-shot-vid2vid

Few-shot Video-to-Video Synthesis

https://github.com/NagabhushanSN95/DPG-Video-Prediction

Disentangling Propagation and Generation for Video Prediction

https://github.com/Nanyangny/CenterTrack-IOU

Simple Online and Realtime Tracking

https://github.com/Ne-ki-tos/Super_res

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Negus25/GAT-Mindspore

Graph Attention Networks

https://github.com/NeosXu/benchmarking_pytorchvideo_and_mmaction2

Hysia: Serving DNN-Based Video-to-Retail Applications in Cloud

https://github.com/Net-Mist/style-transfer-tf2

A Style-Aware Content Loss for Real-time HD Style Transfer

https://github.com/NetraInamdar/Deep_Learning_Project

Visualizing and Understanding Convolutional Networks

https://github.com/Nhat-Thanh/ESPCN-Pytorch

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

https://github.com/Nhat-Thanh/ESPCN-TF

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

https://github.com/Nhat-Thanh/SRCNN-Pytorch

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Nhat-Thanh/SRCNN-TF

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Nhat-Thanh/VDSR-Pytorch

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/NiallEHunt/MonocularDepth-Using-LightFields

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/NikolasEnt/soccernet-calibration-sportlight

SoccerNet 2023 Challenges Results

https://github.com/Nioolek/PPYOLOE_pytorch

PP-YOLOE: An evolved version of YOLO

https://github.com/Niteeshaballa/fastai-practice

Visualizing and Understanding Convolutional Networks

https://github.com/Nithin-GK/UniteandConquer

AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders

https://github.com/Noopuragr/DepthModel

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/Nortinwell/TSN

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

https://github.com/Novemser/deep-imitation

Everybody Dance Now

https://github.com/OML-Team/open-metric-learning

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/OanaIgnat/i3d_keras

The Kinetics Human Action Video Dataset

https://github.com/Oceanlib/DBVI

https://github.com/OfekCohen1/Style-On-3D-Video

ReCoNet: Real-time Coherent Video Style Transfer Network

https://github.com/Oichii/resnet3D_pulse

Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?

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