  We introduce a conditional compression problem and propose a fast framework
for tackling it. The problem is how to quickly compress a pretrained large
neural network into optimal smaller networks given target contexts, e.g. a
context involving only a subset of classes or a context where only limited
compute resource is available. To solve this, we propose an efficient Bayesian
framework to compress a given large network into much smaller size tailored to
meet each contextual requirement. We employ a hypernetwork to parameterize the
posterior distribution of weights given conditional inputs and minimize a
variational objective of this Bayesian neural network. To further reduce the
network sizes, we propose a new input-output group sparsity factorization of
weights to encourage more sparseness in the generated weights. Our methods can
quickly generate compressed networks with significantly smaller sizes than
baseline methods.
