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코드

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

https://github.com/MindSpore-paper-code-3/code10/tree/main/faster_rcnn_ssod

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://github.com/MindSpore-paper-code-3/code10/tree/main/glore_res

RegNet: Self-Regulated Network for Image Classification

https://github.com/MindSpore-paper-code-3/code3/tree/main/faster_rcnn_ssod

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://github.com/MindSpore-paper-code-3/code4/tree/main/googlenet

Going Deeper with Convolutions

https://github.com/MindSpore-paper-code-3/code5/tree/main/ReIDStrongBaseline

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/MindSpore-paper-code-3/code5/tree/main/ResidualAttentionNet

Residual Attention Network for Image Classification

https://github.com/MindSpore-paper-code-3/code5/tree/main/resnetv2

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

https://github.com/MindSpore-paper-code-3/code5/tree/main/resnext152_64x4d

Aggregated Residual Transformations for Deep Neural Networks

https://github.com/MindSpore-paper-code-3/code6/tree/main/3dcnn

Diffusion-Convolutional Neural Networks

https://github.com/MindSpore-paper-code-3/code6/tree/main/AttentionCluster

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

https://github.com/MindSpore-paper-code-3/code6/tree/main/C3D

R-C3D: Region Convolutional 3D Network for Temporal Activity Detection

https://github.com/MindSpore-paper-code-3/code6/tree/main/CBAM

CBAM: Convolutional Block Attention Module

https://github.com/MindSpore-paper-code-3/code6/tree/main/centernet_resnet101

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

https://github.com/MindSpore-paper-code-3/code7/tree/main/GENet_Res50

GeNet: Deep Representations for Metagenomics

https://github.com/MindSpore-paper-code-3/code7/tree/main/faster_rcnn_ssod

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://github.com/MindSpore-paper-code-3/code8/tree/main/ecolite

ECO: Efficient Convolutional Network for Online Video Understanding

https://github.com/MindSpore-paper-code-3/code8/tree/main/faster_rcnn_ssod

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://github.com/MindSpore-paper-code-3/code9/tree/main/ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/MindSpore-paper-code-3/code9/tree/main/textrcnn

Deep Learning Based Text Classification: A Comprehensive Review

https://github.com/MindSpore-scientific-2/code-1/tree/main/Uncertainty_Calibration_Object_Detection

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

https://github.com/MindSpore-scientific-2/code-10/tree/main/Translation-Invariant

On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location

https://github.com/MindSpore-scientific-2/code-10/tree/main/text-to-music

Music Artist Classification with Convolutional Recurrent Neural Networks

https://github.com/MindSpore-scientific-2/code-11/tree/main/Uncertainty_Calibration_Object_Detection

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

https://github.com/MindSpore-scientific-2/code-12/tree/main/Fixup-Initialization

Fixup Initialization: Residual Learning Without Normalization

https://github.com/MindSpore-scientific-2/code-12/tree/main/Learning-with-Noisy-Labels

Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels

https://github.com/MindSpore-scientific-2/code-12/tree/main/fixed-classifier

Fix your classifier: the marginal value of training the last weight layer

https://github.com/MindSpore-scientific-2/code-12/tree/main/from-softmax-to-sparsemax-a-sparse

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

https://github.com/MindSpore-scientific-2/code-14/tree/main/m2m_100

M2m: Imbalanced Classification via Major-to-minor Translation

https://github.com/MindSpore-scientific-2/code-2/tree/main/Uncertainty_Calibration_Object_Detection

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

https://github.com/MindSpore-scientific-2/code-3/tree/main/stable-sam

Sampling Generative Networks

https://github.com/MindSpore-scientific-2/code-4/tree/main/pytorch_convgru

Convolutional Gated Recurrent Units for Medical Relation Classification

https://github.com/MindSpore-scientific-2/code-5/tree/main/Datasets-Models-main

The iNaturalist Species Classification and Detection Dataset

https://github.com/MindSpore-scientific-2/code-5/tree/main/cyclic-cosine-decay-master

Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification

https://github.com/MindSpore-scientific-2/code-8/tree/main/Uncertainty_Calibration_Object_Detection

Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference

https://github.com/MindSpore-scientific/code-1/tree/main/PR_Product

PR Product: A Substitute for Inner Product in Neural Networks

https://github.com/MindSpore-scientific/code-14/tree/main/swish-t

SwishNet: A Fast Convolutional Neural Network for Speech, Music and Noise Classification and Segmentation

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