Diversity Helps: Unsupervised Few-shot Learning via Distribution
Shift-based Data Augmentation

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Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

13 April 2020

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Papers citing "Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation"

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Title
Trainable Class Prototypes for Few-Shot Learning Jianyi Li Guizhong Liu VLM 41 2 0 21 Jun 2021
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning Ze-kun Li Wei Zhao F. Shi Lei Qi Xingzhi Xie ... Yang Gao Shangjie Wu Jun Liu Yinghuan Shi Dinggang Shen 93 60 0 07 Feb 2021
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning Yizhao Gao Nanyi Fei Guangzhen Liu Zhiwu Lu Tao Xiang Songfang Huang 104 37 0 23 Jan 2021
Self-Supervised Prototypical Transfer Learning for Few-Shot Classification Carlos Medina A. Devos Matthias Grossglauser SSL 80 51 0 19 Jun 2020