Zero-Shot Learning/Novelty detection4 [논문리뷰] Adaptive Confidence Smoothing for Generalized Zero-Shot Learning Paper Overview CVPR'19 https://arxiv.org/abs/1812.09903 Adaptive Confidence Smoothing for Generalized Zero-Shot Learning Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples and others are learned from side information, like semantic attributes or text description, in a zero-shot learning fashion (ZSL). Training a sing arxiv.org Abstract 저.. 2024. 3. 25. [논문리뷰] A Semantic Encoding Out-of-Distribution Classifier for Generalized Zero-Shot Learning Paper Overview IEEE Signal Processing Letters'21 https://ieeexplore.ieee.org/document/9464653 Request Rejected ieeexplore.ieee.org Abstract 저자들은 새로운 semantic encoding out-of-distribution classifier (SE-OOD)를 제안한다. 본 방법은 먼저 semantically consistent mapping을 하여 모든 visual sample을 대응되는 semantic attribute에 project한다. 그 다음 projected visual sample과 원래 semantic attribute 둘다 distribution alignment를 위해 lat.. 2024. 3. 18. [논문리뷰] Generalized Zero-Shot Learning Via Over-Complete Distribution Paper Overview CVPR'20 https://arxiv.org/abs/2004.00666 Generalized Zero-Shot Learning Via Over-Complete Distribution A well trained and generalized deep neural network (DNN) should be robust to both seen and unseen classes. However, the performance of most of the existing supervised DNN algorithms degrade for classes which are unseen in the training set. To learn a discr arxiv.org Abstract Zero.. 2024. 3. 11. [논문리뷰] Autoencoder based novelty detection for generalized zero shot learning 이 논문은 autoencoder 기반 novelty detection을 위한 논문이다. ICIP'19 https://ieeexplore.ieee.org/document/8803562 Autoencoder based novelty detection for generalized zero shot learning The problem of generalized zero-shot learning deals with the classification of test examples for which training data may or may not be available. Existing baseline algorithms connect the seen and unseen set of categories by l.. 2024. 2. 5. 이전 1 다음
