385 citations · 1.5k across the 36 of their papers we have counts for
8 papers · 1 filter
Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task
Kenneth Li, Aspen K. Hopkins, David Bau +3
Language models show a surprising range of capabilities, but the source of their apparent competence is unclear. Do these networks just memorize a collection of surface statistics,…
Mass-Editing Memory in a Transformer
Kevin Meng, Arnab Sen Sharma, Alex Andonian +2
Recent work has shown exciting promise in updating large language models with new memories, so as to replace obsolete information or add specialized knowledge. However, this line o…
Content-Based Search for Deep Generative Models
Daohan Lu, Sheng-Yu Wang, Nupur Kumari +4
The growing proliferation of customized and pretrained generative models has made it infeasible for a user to be fully cognizant of every model in existence. To address this need,…
Rewriting Geometric Rules of a GAN
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
Deep generative models make visual content creation more accessible to novice users by automating the synthesis of diverse, realistic content based on a collected dataset. However,…
Local Relighting of Real Scenes
Audrey Cui, Ali Jahanian, Agata Lapedriza +4
We introduce the task of local relighting, which changes a photograph of a scene by switching on and off the light sources that are visible within the image. This new task differs…
Disentangling visual and written concepts in CLIP
Joanna Materzynska, Antonio Torralba, David Bau
The CLIP network measures the similarity between natural text and images; in this work, we investigate the entanglement of the representation of word images and natural images in i…