Papers with Code 아카이브
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/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Network_In_Network
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/On_First_Order_Meta_Learning_Algorithms
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/On_the_Variance_of_the_Adaptive_Learning_Rate_and_Beyond
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Unsupervised_Representation_Learning_with_Deep_Convolutional_Generative_Adversarial_Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MaxwellHeller/coffee_fraud_detection
https://github.com/Mayank57/Pokemon-Generation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Maymaher/StackGANv2
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/Mayurji/Image-Classification-PyTorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mayurji/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/McDonnell-Lab/1-bit-per-weight
https://github.com/Mckinsey666/ACGAN-Anime-Generation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Mckinsey666/Anime-Generation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MeAmarP/Fruit-Classification
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Mechachleopteryx/SpinalNet
https://github.com/Medabid1/CPC
https://github.com/Medabid1/ComplexValuedCNN
https://github.com/Media-Smart/vedaseg
https://github.com/Mees-Molenaar/protein_location
https://github.com/MegEngine/Models/tree/master/official/vision/classification
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet
https://github.com/MegEngine/Models/tree/master/official/vision/detection
https://github.com/MegEngine/Models/tree/master/official/vision/gan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MegEngine/Models/tree/master/official/vision/segmentation
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
https://github.com/Meghraj-Webllisto/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Megvii-Nanjing/ML_GCN
Multi-Label Image Recognition with Graph Convolutional Networks
https://github.com/MehdiChelh/CuMF_SGD
https://github.com/MehdiZouitine/Learning-Disentangled-Representations-via-Mutual-Information-Estimation
Learning Disentangled Representations via Mutual Information Estimation
https://github.com/MenghaoGuo/AutoDeeplab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/MenghaoGuo/EANet
Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks
https://github.com/Meowuu7/Point-Transformer
https://github.com/Mephisto405/Learning-Loss-for-Active-Learning
https://github.com/Mephisto405/Unsupervised-Out-of-Distribution-Detection-by-Maximum-Classifier-Discrepancy
Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy
https://github.com/MiRA-lab-dev/SynRec
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics
https://github.com/MiX-S/resnet_cifar10
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.