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Generalized Zero-Shot Learning
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In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BatsResearch/zsl-kg
공식 코드https://github.com/CZSLwithCVAE/CZSL_CVAE
공식 코드Adversarial Training of Variational Auto-encoders for Continual Zero-shot Learning(A-CZSL)
https://github.com/Hanzy1996/CE-GZSL
공식 코드https://github.com/MKYucel/adversarial_robustness_zsl
공식 코드A Deep Dive into Adversarial Robustness in Zero-Shot Learning
https://github.com/Maunil/LsrGAN
공식 코드Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning
https://github.com/ROBOTICSENGINEER/Open_World_Learning_Without_Labels
공식 코드A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels
https://github.com/ac-rodriguez/zsl_billow
공식 코드Recognition of Unseen Bird Species by Learning from Field Guides
https://github.com/akshitac8/tfvaegan
공식 코드Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification
https://github.com/ali-chr/Transductive_ZSL_3D_Point_Cloud
공식 코드Transductive Zero-Shot Learning for 3D Point Cloud Classification
https://github.com/batsresearch/nayak-tmlr22-code
공식 코드https://github.com/cdb342/dgz
공식 코드https://github.com/cdb342/egzsl
공식 코드https://github.com/cdb342/ijcai-2022-zla
공식 코드https://github.com/chunhuiz/semantics-of-officehome-and-minidomainnet
공식 코드https://github.com/dkurzend/clipclap-gzsl
공식 코드Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models
https://github.com/dvirsamuel/DRAGON
공식 코드From Generalized zero-shot learning to long-tail with class descriptors
https://github.com/edgarschnfld/CADA-VAE-PyTorch
공식 코드Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
https://github.com/francescomanigrass/proto-ltn
공식 코드PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learning
https://github.com/gukyeongkwon/gating-ae
공식 코드A Gating Model for Bias Calibration in Generalized Zero-shot Learning
https://github.com/guyuchao/VAE-cFlow-ZSL
공식 코드Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow
https://github.com/hellowangqian/domain-adaptation-capls
공식 코드Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition
https://github.com/liangjunfeng/srwgan
공식 코드https://github.com/lijin118/AFC-GAN
공식 코드Alleviating Feature Confusion for Generative Zero-shot Learning
https://github.com/lijin118/LisGAN
공식 코드Leveraging the Invariant Side of Generative Zero-Shot Learning
https://github.com/liuyuan-wen/av-ood-gzsl
공식 코드Out-Of-Distribution Detection for Audio-visual Generalized Zero-Shot Learning: A General Framework
https://github.com/manliucoder/psvma
공식 코드Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning
https://github.com/mboboGO/DVBE
공식 코드Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning
https://github.com/mrflogs/SHIP
공식 코드Improving Zero-Shot Generalization for CLIP with Synthesized Prompts
https://github.com/nyuad-cai/CXR-ML-GZSL
공식 코드Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs
https://github.com/osierboy/GEM-ZSL
공식 코드https://github.com/pjbq/d3gzsl
공식 코드Data Distribution Distilled Generative Model for Generalized Zero-Shot Recognition
https://github.com/ramprs/neuron-importance-zsl
공식 코드Choose Your Neuron: Incorporating Domain Knowledge through Neuron-Importance
https://github.com/rfelixmg/frwgan-eccv18
공식 코드https://github.com/schrodingho/fm_zsl_iot
공식 코드https://github.com/shiming-chen/FREE
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.