Need help with SAE?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

136 Stars 41 Forks 87 Commits 2 Opened issues


Semantic Autoencoder for Zero-shot Learning (Spotlight), CVPR 2017

Services available


Need anything else?

Contributors list

# 358,429
85 commits

Semantic Autoencoder for Zero-shot Learning

Elyor Kodirov, Tao Xiang, and Shaogang Gong, Spotlight.


Existing zero-shot learning (ZSL) models typically learn a projection function from a visual 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.

An implementation of SAE in MATLAB

In Python: (Thanks to hoseong-kim)

Download Paper



   author = {Elyor Kodirov, Tao Xiang, and Shagong Gong},
   title = "{Semantic Autoencoder for Zero-shot Learning}",
   journal = {IEEE CVPR 2017},
   year = 2017,
   month = July

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.