6 citations · 6 across the 6 of their papers we have counts for
6 papers · 1 filter
Pristine Models
Anirudh Sundara Rajan, Krishna Kumar Singh, Richard Zhang +1
Training on broad, unfiltered data enables high-quality image generation, but can also teach models concepts that should not be generated; restricting the training data ensures saf…
From Plans to Pixels: Learning to Plan and Orchestrate for Open-Ended Image Editing
Anirudh Sundara Rajan, Krishna Kumar Singh, Yong Jae Lee
Modern image editing models produce realistic results but struggle with abstract, multi step instructions (e.g., ``make this advertisement more vegetarian-friendly''). Prior agent…
VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual Tool Selection
Zeyi Huang, Yuyang Ji, Anirudh Sundara Rajan +5
We introduce VisTA, a new reinforcement learning framework that empowers visual agents to dynamically explore, select, and combine tools from a diverse library based on empirical p…
Stay-Positive: A Case for Ignoring Real Image Features in Fake Image Detection
Anirudh Sundara Rajan, Yong Jae Lee
Detecting AI generated images is a challenging yet essential task. A primary difficulty arises from the detectors tendency to rely on spurious patterns, such as compression artifac…
Aligned Datasets Improve Detection of Latent Diffusion-Generated Images
Anirudh Sundara Rajan, Utkarsh Ojha, Jedidiah Schloesser +1
As latent diffusion models (LDMs) democratize image generation capabilities, there is a growing need to detect fake images. A good detector should focus on the generative models fi…
What Knowledge Gets Distilled in Knowledge Distillation?
Utkarsh Ojha, Yuheng Li, Anirudh Sundara Rajan +2
Knowledge distillation aims to transfer useful information from a teacher network to a student network, with the primary goal of improving the student's performance for the task at…