385 citations · 603 across the 7 of their papers we have counts for
4 papers · 1 filter
When Are Concepts Erased From Diffusion Models?
Kevin Lu, Nicky Kriplani, Rohit Gandikota +4
In concept erasure, a model is modified to selectively prevent it from generating a target concept. Despite the rapid development of new methods, it remains unclear how thoroughly…
One-Step is Enough: Sparse Autoencoders for Text-to-Image Diffusion Models
Viacheslav Surkov, Chris Wendler, Antonio Mari +5
For large language models (LLMs), sparse autoencoders (SAEs) have been shown to decompose intermediate representations that often are not interpretable directly into sparse sums of…
Dissecting Pruned Neural Networks
Jonathan Frankle, David Bau
Pruning is a standard technique for removing unnecessary structure from a neural network to reduce its storage footprint, computational demands, or energy consumption. Pruning can…
On the Units of GANs (Extended Abstract)
David Bau, Jun-Yan Zhu, Hendrik Strobelt +4
Generative Adversarial Networks (GANs) have achieved impressive results for many real-world applications. As an active research topic, many GAN variants have emerged with improveme…