https://github.com/apple/ml-mofi
공식 코드MOFI: Learning Image Representations from Noisy Entity Annotated Images
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/apple/ml-no-token-left-behind
공식 코드No Token Left Behind: Explainability-Aided Image Classification and Generation
https://github.com/apple/ml-sigma-reparam
공식 코드Stabilizing Transformer Training by Preventing Attention Entropy Collapse
https://github.com/apple1986/morsecodeimageclassify
공식 코드https://github.com/apple1986/residual_learning_for_images
공식 코드https://github.com/apple2373/MetaIRNet
공식 코드Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
https://github.com/applied-machine-learning-lab/g3
공식 코드https://github.com/aquastar/csur_bridge_spectral_spatial_gnn_survey
공식 코드Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
https://github.com/ar-roy/dct-cryptonets
공식 코드DCT-CryptoNets: Scaling Private Inference in the Frequency Domain
https://github.com/aramis-lab/AD-ML
공식 코드https://github.com/arangesh/Forced-Spatial-Attention
공식 코드Forced Spatial Attention for Driver Foot Activity Classification
https://github.com/arasmus/ladder
공식 코드https://github.com/araujoalexandre/lipschitz-sll-networks
공식 코드A Unified Algebraic Perspective on Lipschitz Neural Networks
https://github.com/aravindr93/imaml_dev
공식 코드https://github.com/arcelien/pba
공식 코드Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
https://github.com/archiplab-linfengzhang/contrastive-deep-supervision
공식 코드https://github.com/architrathore/TopoAct
공식 코드TopoAct: Visually Exploring the Shape of Activations in Deep Learning
https://github.com/ardhendubehera/sr-gnn
공식 코드SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization
https://github.com/arezaz/ultrasound-texture-analysis
공식 코드https://github.com/arghosh/noisy_label_pretrain
공식 코드Contrastive Learning Improves Model Robustness Under Label Noise
https://github.com/ari-dasci/S-kfc
공식 코드https://github.com/ari-dasci/S-layerwise_cosine_aggregation
공식 코드https://github.com/ari-dasci/s-ddaba
공식 코드Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning
https://github.com/arijitthegame/structured-matrices-peft
공식 코드Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning
https://github.com/aristorenaldo/g-ssl
공식 코드https://github.com/aristotelisballas/cyclemix
공식 코드CycleMix: Mixing Source Domains for Domain Generalization in Style-Dependent Data
https://github.com/aristotelisballas/gga
공식 코드https://github.com/aristotelisballas/m2cl
공식 코드Multi-Scale and Multi-Layer Contrastive Learning for Domain Generalization
https://github.com/arjish/PreTrainedFullLibrary_FewShot
공식 코드https://github.com/arjish/meta-meta-classification
공식 코드https://github.com/arkel23/animesion
공식 코드https://github.com/arkel23/clca
공식 코드Cross-Layer Cache Aggregation for Token Reduction in Ultra-Fine-Grained Image Recognition
https://github.com/armiro/COVID-CXNet
공식 코드COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning
https://github.com/arturjordao/IncrementalDimensionalityReduction
공식 코드Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
https://github.com/arturjordao/WearableSensorData
공식 코드Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art
https://github.com/arunsehrawat/interferometric-neural-networks
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.