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/arnoweng/CheXNet
https://github.com/arobey1/advbench
https://github.com/arobey1/mbrdl
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/arpit2412/Generative-Adversarial-Network-/tree/master/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/arpit2412/InstanceGM
Instance-Dependent Noisy Label Learning via Graphical Modelling
https://github.com/arsenzaryan/CapsNet
https://github.com/artelab/Image-and-Text-fusion-for-UPMC-Food-101-using-BERT-and-CNNs
https://github.com/artem-gorodetskii/Face-Encoder-Decoder
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/artemmavrin/focal-loss
https://github.com/arturjordao/PruningNeuralNetworks
https://github.com/arturjordao/arturjordao
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
https://github.com/artxtech/darknet-rnn
https://github.com/artynet/darknet-alexeyAB
https://github.com/arvieFrydenlund/awd-lstm-lm
https://github.com/aryan-satpathy/backdoor
https://github.com/aryanasadianuoit/MobileNet_V2
https://github.com/aryanasadianuoit/mobilenet_v1
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/as791/Adversarial-Example-Attack-and-Defense
https://github.com/as791/ZOO_Attack_PyTorch
https://github.com/asad-62/IVP-DNN
https://github.com/asahi417/DeepDomainAdaptation
https://github.com/asanakoy/deep_clustering
Deep Clustering for Unsupervised Learning of Visual Features
https://github.com/asarigun/MixerGANsformer
https://github.com/asarigun/TransGAN
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/asd08573064/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/ash3n/Latent-Similarity
https://github.com/ash3n/Meta-Gradients
https://github.com/ash3n/Meta-SGD
https://github.com/ash3n/Meta-SGD-TF
https://github.com/ash3n/Prototypical-Network-TF
https://github.com/ashantanu/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ashaw596/squeezenas
SqueezeNAS: Fast neural architecture search for faster semantic segmentation
https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras
https://github.com/ashishpatel26/GRADCAM-Tensorflow2-Visual-Explainable-AI
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ashishpatel26/Vision-Transformer-Keras-Tensorflow-Pytorch-Examples
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.