Cut Inner Layers: A Structured Pruning Strategy for Efficient U-Net GANs. (arXiv:2206.14658v1 [cs.LG])

Pruning effectively compresses overparameterized models. Despite the success
of pruning methods for discriminative models, applying them for generative
models has been relatively rarely approached. This study conducts structured
pruning on U-Net generators of conditional GANs. A per-layer sensitivity
analysis confirms that many unnecessary filters exist in the innermost layers
near the bottleneck and can be substantially pruned. Based on this observation,
we prune these filters from multiple inner layers or suggest alternative
architectures by completely eliminating the layers. We evaluate …

arxiv gans lg pruning strategy