Invertible Zero-Shot Recognition Flows

• URL: http://arxiv.org/abs/2007.04873v1
• Date: Thu, 9 Jul 2020 15:21:28 GMT
• Title: Invertible Zero-Shot Recognition Flows
• Authors: Yuming Shen, Jie Qin, Lei Huang
• Abstract summary: This work incorporates a new family of generative models (i.e., flow-based models) into Zero-Shot Learning (ZSL) The proposed Invertible Zero-shot Flow (IZF) learns factorized data embeddings with the forward pass of an invertible flow network, while the reverse pass generates data samples. Experiments on widely-adopted ZSL benchmarks demonstrate the significant performance gain of IZF over existing methods.
• Score: 42.839333265321905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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