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

https://github.com/milmor/LDT

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

https://github.com/milmor/LadaGAN-pytorch

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

https://github.com/milmor/diffusion-transformer

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

https://github.com/milmor/diffusion-transformer-keras

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

https://github.com/milmor/ladagan

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

https://github.com/milmor/latent-diffusion-transformer

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

https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/vgg.py

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mindspore-ai/contrib/tree/master/intern/pixelshuffle1d

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

https://github.com/mindspore-ai/models/blob/master/research/cv/glore_res

Graph-Based Global Reasoning Networks

https://github.com/mindspore-ai/models/blob/master/research/cv/glore_res200

Graph-Based Global Reasoning Networks

https://github.com/mindspore-ai/models/blob/master/research/cv/siamRPN

https://github.com/mindspore-ai/models/tree/master/official/cv/Deepsort

Simple Online and Realtime Tracking

https://github.com/mindspore-ai/models/tree/master/official/cv/nima

NIMA: Neural Image Assessment

https://github.com/mindspore-ai/models/tree/master/official/cv/predrnn%2B%2B

PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning

https://github.com/mindspore-ai/models/tree/master/official/cv/srcnn

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/mindspore-ai/models/tree/master/official/cv/vgg16

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mindspore-ai/models/tree/master/official/gnn/gat

Graph Attention Networks

https://github.com/mindspore-ai/models/tree/master/research/cv/AttentionCluster

Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification

https://github.com/mindspore-ai/models/tree/master/research/cv/ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/mindspore-ai/models/tree/master/research/cv/ecolite

ECO: Efficient Convolutional Network for Online Video Understanding

https://github.com/mindspore-ai/models/tree/master/research/cv/fairmot

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/mindspore-ai/models/tree/master/research/cv/r2plus1d

A Closer Look at Spatiotemporal Convolutions for Action Recognition

https://github.com/mindspore-ai/models/tree/master/research/cv/resnet3d

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

https://github.com/mindspore-ai/models/tree/master/research/cv/stnet

StNet: Local and Global Spatial-Temporal Modeling for Action Recognition

https://github.com/mindspore-ai/models/tree/master/research/cv/tsm

TSM: Temporal Shift Module for Efficient Video Understanding

https://github.com/mindspore-ai/models/tree/master/research/cv/tsn

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

https://github.com/mindspore-ai/models/tree/master/research/cv/vgg19

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/swin_transformer_v2.py

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/mindspore-courses/MindSpore-classification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/vgg.py

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mindspore-lab/mindone

Latte: Latent Diffusion Transformer for Video Generation

https://github.com/mindspore-lab/mindone/tree/master/examples/moviegen

Movie Gen: A Cast of Media Foundation Models

https://github.com/mindspore-lab/mindone/tree/master/examples/svd

Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

https://github.com/minghangz/cpl

https://github.com/mingu6/action_seg_ot

Temporal Action Segmentation: An Analysis of Modern Techniques

https://github.com/minhncedutw/pointnet1_keras

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

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