Task-Aware Deep Sampling for Feature Generation
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The human ability to imagine the variety of appearances of novel objects based on past experience is crucial for quickly learning novel visual concepts based on few examples. Endowing machines with a similar ability to generate feature distributions for new visual concepts is key to sample-efficient model generalization. In this work, we propose a novel generator architecture suitable for feature generation in the zero-shot setting. We introduce task-aware deep sampling (TDS) which injects task-aware noise layer-by-layer in the generator, in contrast to existing shallow sampling (SS) schemes where random noise is only sampled once at the input layer of the generator. We propose a sample efficient learning model composed of a TDS generator, a discriminator and a classifier (e.g., a soft-max classifier). We find that our model achieves state-of-the-art results on the compositional zero-shot learning benchmarks as well as improving upon the established benchmarks in conventional zero-shot learning with a faster convergence rate.
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