21 citations · 22 across the 8 of their papers we have counts for
8 papers
ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images
Jinbin Huang, Yuki Ueno, Chen Chen +4
Generative image models can produce highly realistic images, raising concerns about potential misuse in creating deceptive content. Current deepfake approaches face several challen…
Reward Hacking as Equilibrium under Finite Evaluation
Jiacheng Wang, Jinbin Huang
We prove that under five minimal axioms -- multi-dimensional quality, finite evaluation, effective optimization, resource finiteness, and combinatorial interaction -- any optimized…
InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation
Jinbin Huang, Wenbin He, Liang Gou +2
The emergence of large-scale pre-trained models has heightened their application in various downstream tasks, yet deployment is a challenge in environments with limited computation…
ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale
Jinbin Huang, Chen Chen, Aditi Mishra +3
Generative image models have emerged as a promising technology to produce realistic images. Despite potential benefits, concerns grow about its misuse, particularly in generating d…
InterVLS: Interactive Model Understanding and Improvement with Vision-Language Surrogates
Jinbin Huang, Wenbin He, Liang Gou +2
Deep learning models are widely used in critical applications, highlighting the need for pre-deployment model understanding and improvement. Visual concept-based methods, while inc…
PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models
Aditi Mishra, Utkarsh Soni, Anjana Arunkumar +3
Large Language Models (LLMs) have gained widespread popularity due to their ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple natural language prompt.…
ConceptExplainer: Interactive Explanation for Deep Neural Networks from a Concept Perspective
Jinbin Huang, Aditi Mishra, Bum Chul Kwon +1
Traditional deep learning interpretability methods which are suitable for model users cannot explain network behaviors at the global level and are inflexible at providing fine-grai…
Why? Why not? When? Visual Explanations of Agent Behavior in Reinforcement Learning
Aditi Mishra, Utkarsh Soni, Jinbin Huang +1
Reinforcement learning (RL) is used in many domains, including autonomous driving, robotics, stock trading, and video games. Unfortunately, the black box nature of RL agents, combi…