Deep Extrapolation for Attribute-Enhanced Generation

Abstract

TL;DR: How do we generate sequences that extrapolate beyond the training distribution?
Abstract: Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution. We formulate a new task for extrapolation in sequence generation, focusing on natural language and proteins, and propose GENhance, a generative framework that enhances attributes through a learned latent space. Trained on movie reviews and a computed protein stability dataset, GENhance can generate strongly-positive text reviews and highly stable protein sequences without being exposed to similar data during training.

Publication
NeurIPS 2021 (Thirty-Fifth Conference on Neural Information Processing Systems)