Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Kaushal28/Bengali-AI
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/Kaushal28/CutMix-Regularization-using-PyTorch
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/Kaushalya/medclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/KeAWang/BayesianGAN4AdversarialAttacks
https://github.com/KeiraZhao/MDAN
https://github.com/KellerJordan/ResNet-PyTorch-CIFAR10
https://github.com/KellyHwong/rethinking_generalization
https://github.com/KellyYutongHe/Knowledge-Distillation-Net-with-Swish
https://github.com/Kennethborup/BYOL
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/KentaItakura/CNN-classification-using-random-erasing-and-cut-out
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/KentaItakura/Classify-crack-image-and-explain-why-using-MATLAB
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/KernelA/pytorch-point-transformer
https://github.com/Keshavkant/YOLO_Toll
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/KevinMusgrave/pytorch-metric-learning
Momentum Contrast for Unsupervised Visual Representation Learning
https://github.com/KevinMusgrave/pytorch_metric_learning
Momentum Contrast for Unsupervised Visual Representation Learning
https://github.com/KiUngSong/Vision
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Kin-Lau/ILGnet
https://github.com/King-Otaku/Emotion_Recognition_DNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/KingBoyBIT/yolov3test
https://github.com/Kitsunetic/Optimizer-Visualization
https://github.com/Klassikcat/project-NEXTLab-CNN-EfficientNet
https://github.com/Knight825/models-pytorch
https://github.com/KnightZhang625/DARTS_NAS_TextClassification
https://github.com/KnowledgeDiscovery/FaceSec
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/KopiSoftware/Ghost_ResNet56
https://github.com/KostasTok/keras_DCGAN_transfer_learning
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Koukyosyumei/AIJack
https://github.com/Koukyosyumei/Attack_SplitNN
Shredder: Learning Noise Distributions to Protect Inference Privacy
https://github.com/Ksuryateja/DCGAN-CIFAR10-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Ksuryateja/DCGAN-MNIST-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Kungbohan/EECSMed
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/KunlinY/darts
https://github.com/KunlinY/darts-parallel
https://github.com/KurochkinAlexey/AntisymmetricRNN
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
https://github.com/Kurumi233/Mutual-Channel-Loss
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.