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/biboamy/music-repro
공식 코드Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
https://github.com/bidur-khanal/mvaal-medical-images
공식 코드https://github.com/bigvideoresearch/Enigma
공식 코드Webly Supervised Image Classification with Self-Contained Confidence
https://github.com/bigvideoresearch/SCC
공식 코드Webly Supervised Image Classification with Self-Contained Confidence
https://github.com/bilalsoomro/debugging-deep-neural-networks
공식 코드Towards Debugging Deep Neural Networks by Generating Speech Utterances
https://github.com/billpsomas/simpool
공식 코드Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?
https://github.com/bingcongli/vasso
공식 코드Enhancing Sharpness-Aware Optimization Through Variance Suppression
https://github.com/bingykang/Fewshot_Detection
공식 코드https://github.com/binli123/dsmil-wsi
공식 코드https://github.com/biomedia-mbzuai/condisr
공식 코드ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization
https://github.com/biomedia-mira/subgroup-separability
공식 코드The Role of Subgroup Separability in Group-Fair Medical Image Classification
https://github.com/bionlplab/longitudinal_transformer_for_survival_analysis
공식 코드https://github.com/biphasic/quartz-on-loihi
공식 코드Ultra-low-power Image Classification on Neuromorphic Hardware
https://github.com/biqiwhu/agos
공식 코드https://github.com/bit-da/duc
공식 코드Dirichlet-based Uncertainty Calibration for Active Domain Adaptation
https://github.com/bjzhb666/GS-LoRA
공식 코드https://github.com/bknyaz/autocnn_unsup
공식 코드Recursive Autoconvolution for Unsupervised Learning of Convolutional Neural Networks
https://github.com/bl0/negative-margin.few-shot
공식 코드Negative Margin Matters: Understanding Margin in Few-shot Classification
https://github.com/blackfeather-wang/Dynamic-Vision-Transformer
공식 코드Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition
https://github.com/blackfeather-wang/GFNet-Pytorch
공식 코드Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
https://github.com/blackfeather-wang/dynamic-vision-transformer-mindspore
공식 코드Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition
https://github.com/blainehoak/err-on-textures
공식 코드https://github.com/blbadger/depth-representation
공식 코드https://github.com/blgpb/ConGCN
공식 코드Hyperspectral Image Classification With Contrastive Graph Convolutional Network
https://github.com/blindauth/labelshiftexperiments
공식 코드Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation
https://github.com/bluecdm/Long-tailed-recognition
공식 코드https://github.com/bnkausik/occam_gradient_descent
공식 코드https://github.com/bo-liu-tum/encodingnet
공식 코드EncodingNet: A Novel Encoding-based MAC Design for Efficient Neural Network Acceleration
https://github.com/bo-wang-up/prime
공식 코드Topology Optimization of Random Memristors for Input-Aware Dynamic SNN
https://github.com/boa2004plaust/vimd
공식 코드Vision Mamba Distillation for Low-resolution Fine-grained Image Classification
https://github.com/boazlern/SSClustering
공식 코드Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering
https://github.com/bobowner/open-world-lgl
공식 코드https://github.com/bohu615/nu_gan
공식 코드https://github.com/bok3948/gtsa
공식 코드https://github.com/bokveizen/Feedback-Gradient-Descent
공식 코드Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.