https://github.com/aiiu-lab/naturalistic-adversarial-patch
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels
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Model Agnostic Defence against Backdoor Attacks in Machine Learning
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CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
Very Deep Convolutional Networks for Large-Scale Image Recognition
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.