SketchEmbedNet: Learning Novel Concepts by Imitating Drawings

Sketch drawings are an intuitive visual domain that generally preserves semantics. Previous work has shown that recurrent neural networks are capable of producing sketch drawings of a single or few classes at a time. In this work we focus on the representations developed by training a generative model to produce sketches from pixel images across many classes in a sketch domain. We find that the embeddings learned by this sketching model are extremely informative for visual tasks and infer compositional information. We then use them to exceed state-of-the-art performance in unsupervised few-shot classification on the Omniglot and mini-ImageNet benchmarks. We also leverage the generative capacity of our model to produce high quality sketches of novel classes based on just a single example.

Covering up bias in CelebA-like datasets with Markov blankets: A post-hoc cure for attribute prior avoidance

Attribute prior avoidance entails subconscious or willful non-modeling of (meta)attributes that datasets are oft born with, such as the 40 semantic facial attributes associated with the CelebA and CelebA-HQ datasets. The consequences of this infirmity, we discover, are especially stark in state-of-the-art deep generative models learned on these datasets that just model the pixel-space measurements, resulting in an inter-attribute bias-laden latent space. This viscerally manifests itself when we perform face manipulation experiments based on latent vector interpolations. In this paper, we address this and propose a post-hoc solution that utilizes an Ising attribute prior learned in the attribute space and showcase its efficacy via qualitative experiments.