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/xiaomi-automl/SCARLET-NAS
공식 코드https://github.com/xiaoyuanguo/cvad
공식 코드CVAD: A generic medical anomaly detector based on Cascade VAE
https://github.com/xiaozhanguva/Measure-Concentration
공식 코드Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
https://github.com/xiaozhanguva/intrinsic_rob_lu
공식 코드https://github.com/xieenze/DetCo
공식 코드DetCo: Unsupervised Contrastive Learning for Object Detection
https://github.com/xiely-123/shisrcnet
공식 코드https://github.com/xiemk/mlc-pat
공식 코드Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
https://github.com/xijiu9/Train_Transformers_with_INT4
공식 코드https://github.com/xinghaochen/slab
공식 코드https://github.com/xingjunm/dimensionality-driven-learning
공식 코드https://github.com/xingmingyu123456/knowledgeswapping
공식 코드https://github.com/xingpingdong/pl-cfe
공식 코드Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
https://github.com/xinyu1205/robust-loss-mlml
공식 코드Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
https://github.com/xinyulyu/fgpl
공식 코드https://github.com/xinzheli1217/learning-to-self-train
공식 코드Learning to Self-Train for Semi-Supervised Few-Shot Classification
https://github.com/xjtushujun/cmw-net
공식 코드CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
https://github.com/xjtushujun/meta-weight-net
공식 코드Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
https://github.com/xjwu1024/PPT
공식 코드PPT: Token Pruning and Pooling for Efficient Vision Transformers
https://github.com/xl-h/guie-2nd-place-solution
공식 코드https://github.com/xlearning-scu/2024-icml-tac
공식 코드https://github.com/xlhex/gal_syntex
공식 코드https://github.com/xmed-lab/clipn
공식 코드https://github.com/xmed-lab/fed-mas
공식 코드https://github.com/xmed-lab/fopro-kd
공식 코드https://github.com/xrad-ulm/proto-caps
공식 코드Interpretable Medical Image Classification using Prototype Learning and Privileged Information
https://github.com/xrli/hiwl
공식 코드https://github.com/xrt11/xai-code
공식 코드Accurate Explanation Model for Image Classifiers using Class Association Embedding
https://github.com/xtinkt/editable
공식 코드https://github.com/xtotodilex/deep-learning-model-optimization
공식 코드https://github.com/xu-ji/IIC
공식 코드Invariant Information Clustering for Unsupervised Image Classification and Segmentation
https://github.com/xu-ji/subfunctions
공식 코드Test Sample Accuracy Scales with Training Sample Density in Neural Networks
https://github.com/xuanlinli17/large_vlm_distillation_ood
공식 코드Distilling Large Vision-Language Model with Out-of-Distribution Generalizability
https://github.com/xuefeng-li1/Provably-end-to-end-label-noise-learning-without-anchor-points
공식 코드Provably End-to-end Label-Noise Learning without Anchor Points
https://github.com/xuez-phd/focusnet-classifying-better-by-focusing-on-confusing-classes
공식 코드FocusNet: Classifying Better by Focusing on Confusing Classes
https://github.com/xulabs/aitom
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.