3 citations · 8 across the 11 of their papers we have counts for
6 papers · 1 filter
Opt-In Art: Learning Art Styles Only from Few Examples
Hui Ren, Joanna Materzynska, Rohit Gandikota +3
We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples. To investigate this, we train a text-to-ima…
SketchAgent: Language-Driven Sequential Sketch Generation
Yael Vinker, Tamar Rott Shaham, Kristine Zheng +3
Sketching serves as a versatile tool for externalizing ideas, enabling rapid exploration and visual communication that spans various disciplines. While artificial systems have driv…
LoRA vs Full Fine-tuning: An Illusion of Equivalence
Reece Shuttleworth, Jacob Andreas, Antonio Torralba +1
Fine-tuning is a crucial paradigm for adapting pre-trained large language models to downstream tasks. Recently, methods like Low-Rank Adaptation (LoRA) have been shown to effective…
Automatic Discovery of Visual Circuits
Achyuta Rajaram, Neil Chowdhury, Antonio Torralba +2
To date, most discoveries of network subcomponents that implement human-interpretable computations in deep vision models have involved close study of single units and large amounts…
A Multimodal Automated Interpretability Agent
Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang +4
This paper describes MAIA, a Multimodal Automated Interpretability Agent. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpr…
A Vision Check-up for Language Models
Pratyusha Sharma, Tamar Rott Shaham, Manel Baradad +5
What does learning to model relationships between strings teach large language models (LLMs) about the visual world? We systematically evaluate LLMs' abilities to generate and reco…