https://github.com/peymanmorteza/gem
공식 코드Provable Guarantees for Understanding Out-of-distribution Detection
Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/pfjaeger/medicaldetectiontoolkit
공식 코드https://github.com/phbraga/batch-sssom
공식 코드Deep Categorization with Semi-Supervised Self-Organizing Maps
https://github.com/philip-bl/dctn
공식 코드https://github.com/philip-mueller/wsrpn
공식 코드https://github.com/philipchicco/frmil
공식 코드Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification
https://github.com/philippaltmann/sequent
공식 코드SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training
https://github.com/philkr/magic_init
공식 코드Data-dependent Initializations of Convolutional Neural Networks
https://github.com/phuoc-hoan-le/binaryvit
공식 코드BinaryViT: Pushing Binary Vision Transformers Towards Convolutional Models
https://github.com/piacuk/distillistic
공식 코드Teacher Network Calibration Improves Cross-Quality Knowledge Distillation
https://github.com/picekl/DanishFungiDataset
공식 코드Danish Fungi 2020 -- Not Just Another Image Recognition Dataset
https://github.com/pidahbus/deepCBIR
공식 코드https://github.com/pietrobarbiero/entropy-lens
공식 코드https://github.com/pietrobarbiero/logic_explainer_networks
공식 코드https://github.com/pietrobarbiero/pytorch_explain
공식 코드https://github.com/piiswrong/dec
공식 코드https://github.com/pikaplan/LearnExplaiNet
공식 코드Learning local discrete features in explainable-by-design convolutional neural networks
https://github.com/pilancilab/scnn
공식 코드https://github.com/pingqingsheng/LRT
공식 코드https://github.com/piotr-komorowski/towards-evaluating-explanations-of-vit
공식 코드Towards Evaluating Explanations of Vision Transformers for Medical Imaging
https://github.com/piotr310100/epic
공식 코드EPIC: Explanation of Pretrained Image Classification Networks via Prototype
https://github.com/pixwse/tiny2
공식 코드Tiny models from tiny data: Textual and null-text inversion for few-shot distillation
https://github.com/pjjajal/efficientttms
공식 코드https://github.com/pku-icst-mipl/sim-trans_acmmm2022
공식 코드SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization
https://github.com/pkuxmq/ottt-snn
공식 코드https://github.com/pkuyym/evolvingattention
공식 코드https://github.com/pkyriakis/permanifold
공식 코드https://github.com/plai-group/simple-cnaps
공식 코드https://github.com/pmorerio/curriculum-dropout
공식 코드https://github.com/poloclub/viscuit
공식 코드https://github.com/pomonam/Self-Tuning-Networks
공식 코드Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
https://github.com/postBG/DTA.pytorch
공식 코드Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
https://github.com/ppwwyyxx/moco.tensorflow
공식 코드Momentum Contrast for Unsupervised Visual Representation Learning
https://github.com/prakashchhipa/learning-self-supervised-representations-for-label-efficient-cross-domain-knowledge-transfer-on-drf
공식 코드https://github.com/pranavphoenix/Backbones
공식 코드Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision
https://github.com/pranavphoenix/ViX
공식 코드Vision Xformers: Efficient Attention for Image Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.