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Poster
Deep Extrapolation for Attribute-Enhanced Generation
Alvin Chan · Ali Madani · Ben Krause · Nikhil Naik

Thu Dec 09 04:30 PM -- 06:00 PM (PST) @ None #None
in Poster Session 7 »

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. We release our benchmark tasks and models to contribute to the study of generative modeling extrapolation and data-driven design in biology and chemistry.

Keywords: Deep Learning · Generative Model

Author Information

Alvin Chan (Nanyang Technological University)
Ali Madani (Salesforce Research)
Ben Krause (Salesforce)
Nikhil Naik (Salesforce Research)

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