https://github.com/wennyxy/dino-mc
공식 코드Extending global-local view alignment for self-supervised learning with remote sensing imagery
Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 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/wentianzhang-ml/adaptivemix
공식 코드https://github.com/wenxichen2746/Perturbation-Rectified-OOD-Detection
공식 코드Leveraging Perturbation Robustness to Enhance Out-of-Distribution Detection
https://github.com/wer010/Couplformer
공식 코드Couplformer:Rethinking Vision Transformer with Coupling Attention Map
https://github.com/westlake-ai/sema
공식 코드https://github.com/wfwf10/feature-based-federated-transfer-learning
공식 코드Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning
https://github.com/whai362/PVT
공식 코드Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
https://github.com/whuchris/MIA-CTL
공식 코드https://github.com/whut-yirong/espt
공식 코드ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning
https://github.com/whxbergkamp/RobustDL_GAN
공식 코드A Direct Approach to Robust Deep Learning Using Adversarial Networks
https://github.com/wichmann-lab/supervised-learning-dynamics
공식 코드https://github.com/will-doherty/clofai
공식 코드CLOFAI: A Dataset of Real And Fake Image Classification Tasks for Continual Learning
https://github.com/wimh966/QDrop
공식 코드QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
https://github.com/windstormer/mecam-ood
공식 코드https://github.com/winston52/hnm-wsi
공식 코드Hard Negative Sample Mining for Whole Slide Image Classification
https://github.com/winycg/HCGNet
공식 코드Gated Convolutional Networks with Hybrid Connectivity for Image Classification
https://github.com/winycg/HSAKD
공식 코드Knowledge Distillation Using Hierarchical Self-Supervision Augmented Distribution
https://github.com/winycg/MCL-OKD
공식 코드Multi-view Contrastive Learning for Online Knowledge Distillation
https://github.com/winycg/anchornet
공식 코드Localizing Semantic Patches for Accelerating Image Classification
https://github.com/winycg/l-mcl
공식 코드Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
https://github.com/winycg/mcl
공식 코드Mutual Contrastive Learning for Visual Representation Learning
https://github.com/winycg/self-kd-lib
공식 코드MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
https://github.com/wisdeth14/molerecruitment
공식 코드Mole Recruitment: Poisoning of Image Classifiers via Selective Batch Sampling
https://github.com/wiseodd/bayesian_ood_training
공식 코드https://github.com/wiseodd/laplace-bayesopt
공식 코드Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
https://github.com/wjmaddox/drbayes
공식 코드https://github.com/wjun0830/localizable-rotation
공식 코드https://github.com/wjxts/regularizedbn
공식 코드Understanding the Failure of Batch Normalization for Transformers in NLP
https://github.com/wmz9/ire-algorithm-framework
공식 코드Improving Generalization and Convergence by Enhancing Implicit Regularization
https://github.com/wngh1187/ipet
공식 코드Integrated Parameter-Efficient Tuning for General-Purpose Audio Models
https://github.com/wnn2000/fediic
공식 코드FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification
https://github.com/wnn2000/ffl4mia
공식 코드https://github.com/wofmanaf/ResT
공식 코드https://github.com/wojciechowskiofficial/flex
공식 코드Faithful and Plausible Natural Language Explanations for Image Classification: A Pipeline Approach
https://github.com/wolo-wolo/FSFM
공식 코드https://github.com/wonbinkweon/calibratedrankingmodels_aaai2022
공식 코드Obtaining Calibrated Probabilities with Personalized Ranking Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.