  Deep generative models have been successfully applied to Zero-Shot Learning
(ZSL) recently. However, the underlying drawbacks of GANs and VAEs (e.g., the
hardness of training with ZSL-oriented regularizers and the limited generation
quality) hinder the existing generative ZSL models from fully bypassing the
seen-unseen bias. To tackle the above limitations, for the first time, this
work incorporates a new family of generative models (i.e., flow-based models)
into ZSL. The proposed Invertible Zero-shot Flow (IZF) learns factorized data
embeddings (i.e., the semantic factors and the non-semantic ones) with the
forward pass of an invertible flow network, while the reverse pass generates
data samples. This procedure theoretically extends conventional generative
flows to a factorized conditional scheme. To explicitly solve the bias problem,
our model enlarges the seen-unseen distributional discrepancy based on negative
sample-based distance measurement. Notably, IZF works flexibly with either a
naive Bayesian classifier or a held-out trainable one for zero-shot
recognition. Experiments on widely-adopted ZSL benchmarks demonstrate the
significant performance gain of IZF over existing methods, in both classic and
generalized settings.
