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University Of California Santa Barbara Papers

0 research papers from University Of California Santa Barbara that used Prolific for participant recruitment.

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University Of California Santa Barbara

University Of California Santa Barbara: Research Institution — Prolific Citations Library

Browse 2 peer-reviewed papers from University Of California Santa Barbara spanning AI alignment, Representation learning (2024). Research powered by Prolific's high-quality participant data.

This page lists 2 peer-reviewed papers from researchers at University Of California Santa Barbara in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

ImagenHub: Standardizing the evaluation of conditional image generation models

Authors: M Ku, T Li, K Zhang, Y Lu, X Fu, W Zhuang

Year: 2024

Published in: - arXiv preprint arXiv …, 2023 - arxiv.org

Institution: University of Waterloo, Ohio State University, University of California Santa Barbara, University of Pensylvania

Research Area: AI alignment, Representation learning, Cognitive computational modeling, Vision foundation models evaluation, Multimodal, Vision models

Discipline: Computer Science, Artificial Intelligence, Machine Learning

This paper presents a method for **aligning machine vision model representations with human visual similarity judgments across different abstraction levels, improving how well models reflect human perceptual and conceptual organization and enhancing generalization and uncertainty prediction.

DOI: https://doi.org/10.48550/arXiv.2310.01596

Citations: 59
Organic or diffused: Can we distinguish human art from ai-generated images?

Authors: AYJ Ha, J Passananti, R Bhaskar, S Shan

Year: 2024

Published in: Proceedings of the ..., 2024 - dl.acm.org

Institution: University of California Santa Barbara, The University of Chicago, Institute of Education, University College London

Research Area: Human-Computer Interaction, Generative AI, Digital Forensics

Discipline: Human-Computer Interaction, Generative AI, Digital Forensics

The paper investigates the effectiveness of different approaches, including both human and automated detectors, in distinguishing human art from AI-generated images, finding that a combination of methods offers the best performance despite persistent weaknesses.

Methods: Comparison of human art across 7 styles with AI-generated images from 5 generative models, assessed using 5 automated detectors and 3 human groups (crowdworkers, professional artists, expert artists).

Key Findings: Detection accuracy and robustness of human and automated methods in identifying AI-generated images under benign and adversarial conditions.

DOI: 10.1145/3658644.3670306

Citations: 52

Sample Size: 3993