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/avichapman/feature-magnitude-regularization
공식 코드https://github.com/avkskvl/das-f-model
공식 코드A Diff-Attention Aware State Space Fusion Model for Remote Sensing Classification
https://github.com/avrahamraviv/formal-verification-of-object-detection
공식 코드Formal Verification of Deep Neural Networks for Object Detection
https://github.com/awenbocc/llm-ood
공식 코드https://github.com/ayaanzhaque/EC-GAN
공식 코드EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs
https://github.com/ayanc/edgeml.mdp
공식 코드Real-Time Edge Classification: Optimal Offloading under Token Bucket Constraints
https://github.com/azencot-group/crlm
공식 코드https://github.com/azshue/TPT
공식 코드Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
https://github.com/b-faye/prior-knowledge-norm
공식 코드Enhancing Neural Network Representations with Prior Knowledge-Based Normalization
https://github.com/bGhorbani/linearized_neural_networks
공식 코드https://github.com/baai-dcai/bunny
공식 코드https://github.com/baai-dcai/dataset-pruning
공식 코드https://github.com/baaivision/EVA/tree/master/EVA-CLIP-18B
공식 코드https://github.com/baaivision/diva
공식 코드https://github.com/baaivision/eva
공식 코드EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/bach05/SOODImageNet
공식 코드https://github.com/badripatro/efficient360
공식 코드https://github.com/baithebest/samc
공식 코드https://github.com/baithebest/srdml
공식 코드https://github.com/balhaekim/bpc-divergences
공식 코드https://github.com/baohaoliao/mefts
공식 코드Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
https://github.com/bapi/lpp-sgd
공식 코드Scaling the Wild: Decentralizing Hogwild!-style Shared-memory SGD
https://github.com/barleah/greedyal
공식 코드Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval
https://github.com/barulalithb/stereo-angular-margin
공식 코드Improvising the Learning of Neural Networks on Hyperspherical Manifold
https://github.com/basiralab/reptreefl
공식 코드https://github.com/basiralab/unifed
공식 코드https://github.com/batsresearch/fudd
공식 코드Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification
https://github.com/batsresearch/menghini-enhanceclipwithclip-code
공식 코드Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning
https://github.com/batsresearch/menghini-neurips23-code
공식 코드Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning
https://github.com/batsresearch/piriyakulkij-mlsys22-code
공식 코드TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data
https://github.com/batsresearch/taglets
공식 코드TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data
https://github.com/bayu-ladom-ipok/weOpen
공식 코드https://github.com/bazinga699/ncl
공식 코드Nested Collaborative Learning for Long-Tailed Visual Recognition
https://github.com/bbbdylan/unisiam
공식 코드https://github.com/bbrattoli/JigsawPuzzlePytorch
공식 코드Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.