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/freder-chen/vitp
https://github.com/freeSubmission/SDB
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
https://github.com/frgfm/Holocron
https://github.com/frgfm/torch-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ftorres11/saliencysense
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/ftramer/Handcrafted-DP
https://github.com/ftramer/ad-versarial
Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning
https://github.com/fudan-zvg/DGMN2_MindSpore_Ascend
Dynamic Graph Message Passing Networks for Visual Recognition
https://github.com/fujenchu/matchingNet
https://github.com/funalab/PredictMovingDirection
https://github.com/fungtion/DANN
https://github.com/fupiao1998/res2net-keras
https://github.com/fursovia/tcav_nlp
https://github.com/fushenghao/cpea
https://github.com/fushengwuyu/R-Drop
https://github.com/fvisin/reseg
ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
https://github.com/fxfviolet/Densenet_for_quiz_classification
https://github.com/fznsakib/great-ape-behaviour-detector
https://github.com/g-u-n/pycil
https://github.com/g4vrel/AAE
https://github.com/g4vrel/simple_ml/tree/main/InfoGAN
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/g8a9/ferret
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/gaasher/data2vec2.0_vision
https://github.com/gaborvecsei/Barlow-Twins
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/gabrieldernbach/barlow-twins
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/gabrieletiboni/Domain-adaptation-on-PACS-dataset
https://github.com/gabrielhuang/reptile-pytorch
https://github.com/gaetandi/cheXpert
https://github.com/gagan16/DcGan-Tensorflow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/gakkiri/Filter-Response-Normalization
https://github.com/galdeia/iirsbenchmark
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://github.com/gan3sh500/attention-augmented-conv
https://github.com/gan3sh500/custom-pooling
https://github.com/gan3sh500/dropblock
DropBlock: A regularization method for convolutional networks
https://github.com/gan3sh500/local-relational-nets
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.