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/JiwonCocoder/label_transformer
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
https://github.com/Jmak12/Iris1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Jo-dsa/SemanticSeg
https://github.com/JoakimHaurum/TokenReduction
Which Tokens to Use? Investigating Token Reduction in Vision Transformers
https://github.com/JoeTracey/Portfolio
https://github.com/JoegameZhou/Levit
LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
https://github.com/JoegameZhou/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/JoegameZhou/mPanGu-Alpha-53
https://github.com/JoegameZhou/ms-resnetv2
https://github.com/JoeriHermans/dist-keras
https://github.com/JoeyMcNatt/Deep-Learning-Final-Project-Maloney-McNatt-Satriale
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
https://github.com/JohanPhan/Dual_active_sampling_Active_learning
https://github.com/JohnBensen1000/machine_learning
https://github.com/JohnYKiyo/VAT
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
https://github.com/JojiJoseph/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/JonasWechsler/DeepLearningLab5
https://github.com/Jongchan/attention-module
https://github.com/JoonHyung-Park/DeepEnsemble
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/JoonHyung-Park/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Jopapy19/CNN-App
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Jorge-Mendes/darknet-google-colab
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/JorisRoels/domain-adaptive-segmentation
Learning Transferable Features with Deep Adaptation Networks
https://github.com/JosephElHachem/GradCam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/JuHyun-E/Attention-ResNet50
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/JuHyun-E/Attention_in_NN
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/JuanCasian/ML-Speech-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/JudyYe/zero-shot-gcn
Zero-Shot Learning by Convex Combination of Semantic Embeddings
https://github.com/Julien-pour/music_classifcation
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/JunYeopLee/capsule-networks
https://github.com/Junghwan-brian/SDE-Net
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/JunnYu/x-transformers-paddle
https://github.com/JunrQ/NAS
https://github.com/JunsikChoi/Pytorch-InfoGAN
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/JunsikChoi/Pytorch-InfoGAN-CR
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/JunweiLiang/Object_Detection_Tracking
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.