Before revision:  We use an autoencoder to model the variation in the control, and then interfere with neuron activations on inference to correct for these differences on the entire sample. This technique , which we term neuron interference, is unique in its ability to generalize a batch effect learned on a subpopulation to the entire population .
After revision:  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 showcase our technique on image domain/style transfer and two biological applications: removal of batch artifacts representing unwanted noise and modeling the effect of drug treatments to predict synergy between drugs .