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/zxj32/uncertainty-GNN
공식 코드https://github.com/zxk1212/OODD
공식 코드OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary
https://github.com/zyh-uaiaaaa/MDA-noisy-label-learning
공식 코드https://github.com/zyh-uaiaaaa/erasing-attention-consistency
공식 코드Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
https://github.com/zyn-1101/dandr
공식 코드https://github.com/zyxxmu/White-Box
공식 코드https://github.com/zzd1992/Adversarial-Defense-by-Suppressing-High-Frequencies
공식 코드Adversarial Defense by Suppressing High-frequency Components
https://github.com/zzm11/mc-mlp
공식 코드MC-MLP:Multiple Coordinate Frames in all-MLP Architecture for Vision
https://github.com/zzp1012/label-noise-theory
공식 코드https://github.com/zzzace2000/FIDO-saliency
공식 코드https://github.com/zzzace2000/robust_cls_model
공식 코드Towards Robust Classification Model by Counterfactual and Invariant Data Generation
https://github.com/zzzzms/LocalClustering4LungCancer
공식 코드Local Clustering for Lung Cancer Image Classification via Sparse Solution Technique
https://gitlab.com/Avya/deepher2
공식 코드https://gitlab.com/Bitterwolf/GOOD
공식 코드Certifiably Adversarially Robust Detection of Out-of-Distribution Data
https://gitlab.com/Queuecumber/jpeg-domain-resnet
공식 코드https://gitlab.com/adriaruizo/dhne-aaai21
공식 코드Anytime Inference with Distilled Hierarchical Neural Ensembles
https://gitlab.com/cioflan/ms-ranas
공식 코드MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search
https://gitlab.com/dxxz/Self-Supervised-GAN-Compression-Appendix
공식 코드https://gitlab.com/monogenic-layer-m6/monogenic-layer-trainablebio-inspired-cnnlayerforcontrastinvariance
공식 코드https://gitlab.com/nbatfai/smnist
공식 코드https://gitlab.com/saviola/rocov2-code
공식 코드ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset
https://gitlab.com/thomio/2srelu
공식 코드https://gitlab.com/viper-purdue/lic-pruning
공식 코드Structured Pruning and Quantization for Learned Image Compression
https://gitlab.com/viper-purdue/ocil-real-world-food-image-classification
공식 코드Online Class-Incremental Learning For Real-World Food Image Classification
https://gitlab.kitware.com/darpa_learn/learn
공식 코드LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning
https://huggingface.co/diabolic6045/indian_cities_image_classification
공식 코드https://huggingface.co/enalis/scold
공식 코드A Vision-Language Foundation Model for Leaf Disease Identification
https://huggingface.co/geolocal/StreetCLIP
공식 코드Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization
https://notabug.org/jbowren/hv2model
공식 코드https://worksheets.codalab.org/worksheets/0x2ae89944846444539c2d08a0b7ff3f6f
공식 코드https://worksheets.codalab.org/worksheets/0x7b8f6fbc6b5c49c18ac7ca94aafaa1a7
공식 코드https://worksheets.codalab.org/worksheets/0xa21e794020bb474d8804ec7bc0543f52
공식 코드https://worksheets.codalab.org/worksheets/0xbcd424d2bf544c4786efcc0063759b1a
공식 코드https://zenodo.org/record/7981159
공식 코드A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images
https://bitbucket.org/tsungyu/bcnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.