2.3k citations · 7.8k across the 98 of their papers we have counts for
11 papers · 1 filter
Prioritized Generative Replay
Renhao Wang, Kevin Frans, Pieter Abbeel +2
Sample-efficient online reinforcement learning often uses replay buffers to store experience for reuse when updating the value function. However, uniform replay is inefficient, sin…
IT: Idempotent Test-Time Training
Nikita Durasov, Assaf Shocher, Doruk Oner +3
Deep learning models often struggle when deployed in real-world settings due to distribution shifts between training and test data. While existing approaches like domain adaptation…
Evaluating Multiview Object Consistency in Humans and Image Models
Tyler Bonnen, Stephanie Fu, Yutong Bai +5
We introduce a benchmark to directly evaluate the alignment between human observers and vision models on a 3D shape inference task. We leverage an experimental design from the cogn…
Diffusion Models as Data Mining Tools
Ioannis Siglidis, Aleksander Holynski, Alexei A. Efros +2
This paper demonstrates how to use generative models trained for image synthesis as tools for visual data mining. Our insight is that since contemporary generative models learn an…
Rethinking Score Distillation as a Bridge Between Image Distributions
David McAllister, Songwei Ge, Jia-Bin Huang +4
Score distillation sampling (SDS) has proven to be an important tool, enabling the use of large-scale diffusion priors for tasks operating in data-poor domains. Unfortunately, SDS…
Interpreting the Weight Space of Customized Diffusion Models
Amil Dravid, Yossi Gandelsman, Kuan-Chieh Wang +4
We investigate the space of weights spanned by a large collection of customized diffusion models. We populate this space by creating a dataset of over 60,000 models, each of which…
Data Attribution for Text-to-Image Models by Unlearning Synthesized Images
Sheng-Yu Wang, Aaron Hertzmann, Alexei A. Efros +2
The goal of data attribution for text-to-image models is to identify the training images that most influence the generation of a new image. Influence is defined such that, for a gi…
Interpreting the Second-Order Effects of Neurons in CLIP
Yossi Gandelsman, Alexei A. Efros, Jacob Steinhardt
We interpret the function of individual neurons in CLIP by automatically describing them using text. Analyzing the direct effects (i.e. the flow from a neuron through the residual…
Toon3D: Seeing Cartoons from New Perspectives
Ethan Weber, Riley Peterlinz, Rohan Mathur +3
We recover the underlying 3D structure from images of cartoons and anime depicting the same scene. This is an interesting problem domain because images in creative media are often…
Disentangled 3D Scene Generation with Layout Learning
Dave Epstein, Ben Poole, Ben Mildenhall +2
We introduce a method to generate 3D scenes that are disentangled into their component objects. This disentanglement is unsupervised, relying only on the knowledge of a large pretr…
Rethinking Patch Dependence for Masked Autoencoders
Letian Fu, Long Lian, Renhao Wang +6
In this work, we examine the impact of inter-patch dependencies in the decoder of masked autoencoders (MAE) on representation learning. We decompose the decoding mechanism for mask…