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/okankop/CNN-layer-reuse
공식 코드https://github.com/okojoalg/dfformer
공식 코드https://github.com/okojoalg/sequencer
공식 코드https://github.com/olawalesalaudeen/imagenot
공식 코드https://github.com/oleksandr-balabanov/stochastic-ensembles
공식 코드https://github.com/olgarithmics/ICLR_CAMIL
공식 코드https://github.com/oliverrensu/deepmim
공식 코드https://github.com/oliverrensu/tinymim
공식 코드TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models
https://github.com/olivesgatech/Intelligent-Multi-View-TTA
공식 코드https://github.com/olivesgatech/scribe-robustness-overfit
공식 코드Robustness and Overfitting Behavior of Implicit Background Models
https://github.com/omarcastano/elevation-aware-ssl
공식 코드https://github.com/omarsherif200/hdkd
공식 코드HDKD: Hybrid Data-Efficient Knowledge Distillation Network for Medical Image Classification
https://github.com/omerb01/transboost
공식 코드TransBoost: Improving the Best ImageNet Performance using Deep Transduction
https://github.com/omidiu/ca_tome
공식 코드https://github.com/omidmnezami/pick-object-attack
공식 코드Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection
https://github.com/omipan/svl_adapter
공식 코드SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models
https://github.com/ondratybl/vkdnw
공식 코드Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights
https://github.com/onucharles/tensorized-rnn
공식 코드https://github.com/onuriel/PermutedAdaIN
공식 코드Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification
https://github.com/oopshell/Pixel-Blocks-Mixing-For-Image-Privacy-Preservation
공식 코드https://github.com/oozdenizci/pall
공식 코드https://github.com/open-debin/bayesian_mqda
공식 코드Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
https://github.com/open-mmlab/mmdetection
공식 코드https://github.com/openai/CLIP
공식 코드Learning Transferable Visual Models From Natural Language Supervision
https://github.com/openai/improved-gan
공식 코드https://github.com/openai/improved_gan
공식 코드https://github.com/openai/supervised-reptile
공식 코드https://github.com/opengvlab/dcnv4
공식 코드Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications
https://github.com/opengvlab/diffrate
공식 코드DiffRate : Differentiable Compression Rate for Efficient Vision Transformers
https://github.com/opengvlab/internimage
공식 코드InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
https://github.com/opengvlab/piip
공식 코드https://github.com/openmedlab/MedFM
공식 코드https://github.com/opennlplab/hgrn2
공식 코드https://github.com/opennlplab/vicinity-vision-transformer
공식 코드https://github.com/opensource12345678/why_cosine_works
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.