Zero-Shot Learning with Generative Latent Prototype Model
By Yanan Li, Donghui Wang. 2017
16 citations recorded by the source corpus.
Zero-shot learning, which studies the problem of object classification for categories for which we have no training examples, is gaining increasing attention from community. Most existing ZSL methods exploit deterministic transfer learning via an in-between semantic embedding space. In this paper, we try to attack this problem from a generative probabilistic modelling perspective. We assume for any category, the observed representation, e.g. images or texts, is developed from a unique prototype in a latent space, in which the semantic relationship among prototypes is encoded via linear reconstruction. Taking advantage of this assumption, virtual instances of unseen classes can be generated from the corresponding prototype, giving rise to a novel ZSL model which can alleviate the domain shift problem existing in the way of direct transfer learning. Extensive experiments on three benchmark datasets show our proposed model can achieve state-of-the-art results.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Zeynep Akata
- Bernt Schiele
- Piyush Rai
- Yongqin Xian
- Lawrence Carin
- Vinay Kumar Verma
- Chunyuan Li
- Trevor Darrell
In Computer Vision and Pattern Recognition
- Zeynep Akata
- Lawrence Carin
- Bernt Schiele
- Chunyuan Li
- Trevor Darrell
- Luc Van Gool
- Dacheng Tao
- Yi Yang
In Radiology, Nuclear Medicine and Imaging
- Zeynep Akata
- Piyush Rai
- Bernt Schiele
- Yongqin Xian
- Vinay Kumar Verma
- Christoph H. Lampert
- Lawrence Carin
- Yizhe Zhang
Citations in the corpus
- Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions (2017, background): “In fact there is a growing literature discussing the issues mentioned above for AM (Montavon, Samek, and Müller 2017).”
- Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly (2017, background): “GLaP [42] assumes that each class-conditional distribution follows a”
- Zero-Shot Learning via Class-Conditioned Deep Generative Models (2017, methodology): “Some other recent works have explored the idea of generative models for zero-shot learning (Li et al. 2017; Verma et al. 2017).”