Semantic Autoencoder for Zero-Shot Learning
Elyor Kodirov,Tao Xiang,Shaogang Gong +2 more
- pp 4447-4456
TLDR
In this paper, an encoder aims to project a visual feature vector into the semantic space as in the existing ZSL models, but the decoder exerts an additional constraint, that the projection/code must be able to reconstruct the original visual feature.Abstract:
Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space (e.g. attribute space). However, such a projection function is only concerned with predicting the training seen class semantic representation (e.g. attribute prediction) or classification. When applied to test data, which in the context of ZSL contains different (unseen) classes without training data, a ZSL model typically suffers from the project domain shift problem. In this work, we present a novel solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the encoder-decoder paradigm, an encoder aims to project a visual feature vector into the semantic space as in the existing ZSL models. However, the decoder exerts an additional constraint, that is, the projection/code must be able to reconstruct the original visual feature. We show that with this additional reconstruction constraint, the learned projection function from the seen classes is able to generalise better to the new unseen classes. Importantly, the encoder and decoder are linear and symmetric which enable us to develop an extremely efficient learning algorithm. Extensive experiments on six benchmark datasets demonstrate that the proposed SAE outperforms significantly the existing ZSL models with the additional benefit of lower computational cost. Furthermore, when the SAE is applied to supervised clustering problem, it also beats the state-of-the-art.read more
Citations
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Proceedings ArticleDOI
Learning to Compare: Relation Network for Few-Shot Learning
TL;DR: A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning.
Posted Content
Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly
TL;DR: A new zero-shot learning dataset is proposed, the Animals with Attributes 2 (AWA2) dataset which is made publicly available both in terms of image features and the images themselves and compares and analyzes a significant number of the state-of-the-art methods in depth.
Journal ArticleDOI
Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly
TL;DR: The Animals with Attributes 2 (AWA2) dataset as mentioned in this paper is a new dataset for zero-shot learning, which is publicly available both in terms of image features and the images themselves.
Proceedings ArticleDOI
Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs
TL;DR: In this article, a graph convolutional network (GCN) is used to predict the visual classifiers of unseen categories, which is robust to noise in the learned knowledge graph (KG) given a semantic embedding for each node (representing visual category).
Proceedings ArticleDOI
Generalized Zero-Shot Learning via Synthesized Examples
TL;DR: This work presents a generative framework for generalized zero-shot learning where the training and test classes are not necessarily disjoint, and can generate novel exemplars from seen/unseen classes, given their respective class attributes.
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