Discriminative k-shot learning using probabilistic models
作者： Matthias Bauer , Mateo Rojas-Carulla , Jakub Bartłomiej Świątkowski , Bernhard Schölkopf , Richard E Turner
DOI:
关键词: Contextual image classification 、 Artificial neural network 、 Probabilistic analysis of algorithms 、 Artificial intelligence 、 Discriminative model 、 Probabilistic logic 、 Machine learning 、 Probabilistic relevance model 、 Computer science 、 Probabilistic classification 、 Statistical model 、 Probabilistic CTL 、 Divergence-from-randomness model
摘要: This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new …
arxiv.org
harvard.edu参考文章(2)
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition computer vision and pattern recognition. pp. 770- 778 ,(2016) , 10.1109/CVPR.2016.90
Hugo Larochelle, Sachin Ravi, Optimization as a Model for Few-Shot Learning international conference on learning representations. ,(2017)