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/workingcoder/MCJA
공식 코드https://github.com/wssun/sslbackdoormitigation
공식 코드On the Effectiveness of Distillation in Mitigating Backdoors in Pre-trained Encoder
https://github.com/wtaozhao/geomix
공식 코드https://github.com/wtl666wtl/WOT-Class
공식 코드https://github.com/wu-yu-hao/2024_icml_tss
공식 코드Mitigating Label Noise on Graph via Topological Sample Selection
https://github.com/wuhanstudio/adversarial-classification
공식 코드Distributed Black-box Attack: Do Not Overestimate Black-box Attacks
https://github.com/wusize/clipself
공식 코드CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
https://github.com/wutong16/DistributionBalancedLoss
공식 코드Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
https://github.com/wuwushrek/taylanets
공식 코드https://github.com/wuyushuwys/tor_ssm
공식 코드https://github.com/wuyxin/falcon-glfrontiers
공식 코드https://github.com/wvangansbeke/Unsupervised-Classification
공식 코드https://github.com/wvuvl/DARMA
공식 코드https://github.com/wwoods/adversarial-explanations-cifar
공식 코드https://github.com/wxitxy/complementary_intra-class_and_inter-class_mixup
공식 코드https://github.com/wxitxy/synermix
공식 코드https://github.com/wyharveychen/CloserLookFewShot
공식 코드https://github.com/wykk00/graphbridge
공식 코드Bridging Local Details and Global Context in Text-Attributed Graphs
https://github.com/wyn430/CPAC-CNN
공식 코드Tensor decomposition to Compress Convolutional Layers in Deep Learning
https://github.com/wyn430/wood
공식 코드https://github.com/wzhouad/Contra-OOD
공식 코드Contrastive Out-of-Distribution Detection for Pretrained Transformers
https://github.com/wzn0828/PR_Product
공식 코드PR Product: A Substitute for Inner Product in Neural Networks
https://github.com/wzzheng/idml
공식 코드https://github.com/wzzheng/pdml
공식 코드Probabilistic Deep Metric Learning for Hyperspectral Image Classification
https://github.com/xbyym/dlsr
공식 코드Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection
https://github.com/xialeiliu/Awesome-Incremental-Learning
공식 코드Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy
https://github.com/xiangly55/LFME
공식 코드Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification
https://github.com/xiangyongcao/CNN_HSIC_MRF
공식 코드Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
https://github.com/xiangyu8/PT-MAP-sf
공식 코드Few-Shot Learning by Integrating Spatial and Frequency Representation
https://github.com/xianyongqin/few-shot-video-classification
공식 코드Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation
https://github.com/xiaoboxia/T-Revision
공식 코드Are Anchor Points Really Indispensable in Label-Noise Learning?
https://github.com/xiaoboxia/rtm_lnl
공식 코드Regularly Truncated M-estimators for Learning with Noisy Labels
https://github.com/xiaohua-chen/risda
공식 코드https://github.com/xiaojinying/SEVEN
공식 코드https://github.com/xiaomi-automl/MoGA
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.