  Zero-shot Learning (ZSL) classification categorizes or predicts classes
(labels) that are not included in the training set (unseen classes). Recent
works proposed different semantic autoencoder (SAE) models where the encoder
embeds a visual feature vector space into the semantic space and the decoder
reconstructs the original visual feature space. The objective is to learn the
embedding by leveraging a source data distribution, which can be applied
effectively to a different but related target data distribution. Such
embedding-based methods are prone to domain shift problems and are vulnerable
to biases. We propose an integral projection-based semantic autoencoder
(IP-SAE) where an encoder projects a visual feature space concatenated with the
semantic space into a latent representation space. We force the decoder to
reconstruct the visual-semantic data space. Due to this constraint, the
visual-semantic projection function preserves the discriminatory data included
inside the original visual feature space. The enriched projection forces a more
precise reconstitution of the visual feature space invariant to the domain
manifold. Consequently, the learned projection function is less domain-specific
and alleviates the domain shift problem. Our proposed IP-SAE model consolidates
a symmetric transformation function for embedding and projection, and thus, it
provides transparency for interpreting generative applications in ZSL.
Therefore, in addition to outperforming state-of-the-art methods considering
four benchmark datasets, our analytical approach allows us to investigate
distinct characteristics of generative-based methods in the unique context of
zero-shot inference.
