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/Jumabek/wbc_rnn
공식 코드W-Net: A CNN-based Architecture for White Blood Cells Image Classification
https://github.com/Jwicaksana/FedNPR
공식 코드Non-parametric regularization for class imbalance federated medical image classification
https://github.com/K-H-Ismail/torchortho
공식 코드https://github.com/KMnP/vpt
공식 코드https://github.com/KRLGroup/memory-wrap
공식 코드Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models
https://github.com/KaidongLi/pytorch-LatticePointClassifier
공식 코드Robust 3D Point Clouds Classification based on Declarative Defenders
https://github.com/KaihuaTang/Long-Tailed-Recognition.pytorch
공식 코드Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
https://github.com/KaimingHe/resnet-1k-layers
공식 코드https://github.com/KaiqiZhang/admm-pruning
공식 코드A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
https://github.com/Kaleidophon/know-your-limits
공식 코드Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection
https://github.com/KaosEngineer/PriorNetworks
공식 코드Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
https://github.com/KentoNishi/Augmentation-for-LNL
공식 코드https://github.com/KeremTurgutlu/clip_art
공식 코드CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification
https://github.com/KeremTurgutlu/self_supervised
공식 코드CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification
https://github.com/KevinCarpricorn/EchoAlign
공식 코드https://github.com/Kevinz-code/CSRA
공식 코드Residual Attention: A Simple but Effective Method for Multi-Label Recognition
https://github.com/KhrulkovV/hyperbolic-image-embeddings
공식 코드https://github.com/KieDani/SegformerPlusPlus
공식 코드Segformer++: Efficient Token-Merging Strategies for High-Resolution Semantic Segmentation
https://github.com/King-HAW/DC-MT
공식 코드https://github.com/KingJamesSong/DifferentiableSVD
공식 코드On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition
https://github.com/KrishnaTarun/Unification
공식 코드Unifying Synergies between Self-supervised Learning and Dynamic Computation
https://github.com/Kthyeon/FINE_official
공식 코드https://github.com/Kthyeon/fedsup
공식 코드Supernet Training for Federated Image Classification under System Heterogeneity
https://github.com/KunalP117/DL_RI
공식 코드Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification
https://github.com/KyanChen/RSMamba
공식 코드RSMamba: Remote Sensing Image Classification with State Space Model
https://github.com/KyleZheng1997/ReSSL
공식 코드ReSSL: Relational Self-Supervised Learning with Weak Augmentation
https://github.com/KyleZheng1997/WCL
공식 코드https://github.com/LADI-Dataset/ladi-overview
공식 코드LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery
https://github.com/LKJacky/Differentiable-Model-Scaling
공식 코드https://github.com/LOG-postech/safe-torch
공식 코드https://github.com/LTS4/DeepFool
공식 코드DeepFool: a simple and accurate method to fool deep neural networks
https://github.com/LTS4/SparseFool
공식 코드https://github.com/LUMII-Syslab/Matrix-SE
공식 코드https://github.com/LUMII-Syslab/Switchblade
공식 코드https://github.com/LandskapeAI/triplet-attention
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.