Out-of-Sample Extrapolation with Neuron Editing

TL;DR

This paper introduces neuron editing, a method that enables neural networks to extrapolate transformations outside their training data by manipulating neuron activations in a latent space, with applications in image editing and biological data analysis.

Contribution

The paper presents neuron editing, a novel technique for out-of-sample extrapolation by learning how neurons encode transformations, enabling complex edits in a simplified latent space.

Findings

Neuron editing successfully extrapolates transformations outside training data.

The method effectively removes batch artifacts in biological datasets.

It predicts drug synergy effects from latent space manipulations.

Abstract

While neural networks can be trained to map from one specific dataset to another, they usually do not learn a generalized transformation that can extrapolate accurately outside the space of training. For instance, a generative adversarial network (GAN) exclusively trained to transform images of black-haired men to blond-haired men might not have the same effect on images of black-haired women. This is because neural networks are good at generation within the manifold of the data that they are trained on. However, generating new samples outside of the manifold or extrapolating "out-of-sample" is a much harder problem that has been less well studied. To address this, we introduce a technique called neuron editing that learns how neurons encode an edit for a particular transformation in a latent space. We use an autoencoder to decompose the variation within the dataset into activations of different neurons and generate transformed data by defining an editing transformation on those neurons. By performing the transformation in a latent trained space, we encode fairly complex and non-linear transformations to the data with much simpler distribution shifts to the neuron's activations. We motivate our technique on an image domain and then move to our two main biological applications: removal of batch artifacts representing unwanted noise and modeling the effect of drug treatments to predict synergy between drugs.