  Zero-shot learning strives to classify unseen categories for which no data is
available during training. In the generalized variant, the test samples can
further belong to seen or unseen categories. The state-of-the-art relies on
Generative Adversarial Networks that synthesize unseen class features by
leveraging class-specific semantic embeddings. During training, they generate
semantically consistent features, but discard this constraint during feature
synthesis and classification. We propose to enforce semantic consistency at all
stages of (generalized) zero-shot learning: training, feature synthesis and
classification. We first introduce a feedback loop, from a semantic embedding
decoder, that iteratively refines the generated features during both the
training and feature synthesis stages. The synthesized features together with
their corresponding latent embeddings from the decoder are then transformed
into discriminative features and utilized during classification to reduce
ambiguities among categories. Experiments on (generalized) zero-shot object and
action classification reveal the benefit of semantic consistency and iterative
feedback, outperforming existing methods on six zero-shot learning benchmarks.
Source code at https://github.com/akshitac8/tfvaegan.
