Fine-Grained Retrieval with Autoencoders
Tiziano Portenier, Qiyang Hu, Paolo Favaro, Matthias Zwicker · 2018
In this paper we develop a representation for fine-grained retrieval. Given a query, we want to retrieve data items of the same class, and, in addition, rank these items according to intra-class similarity. In our training data we assume partial knowledge: class labels are available, but the intra-class attributes are not. To compensate for this knowledge gap we propose using an autoencoder, which can be trained to produce features both with and without labels. Our main hypothesis is that network architectures that incorporate an autoencoder can learn features that meaningfully cluster data based on the intra-class variability. We propose and compare different architectures to construct our features, including a Siamese autoencoder (SAE), a classifying autoencoder (CAE) and a separate classifier-autoencoder (SCA). We find that these architectures indeed improve fine grained retrieval compared to features trained purely in a supervised fashion for classification. We perform experiments on four datasets, and observe that the SCA generally outperforms the other two. In particular, we obtain state of the art performance on fine-grained sketch retrieval.