Eugene Choi

Eugene Choi, Julian Rodriguez, Edmund Young

Domain adaptation is an active area of research driven by the growing demand for robust machine learning models that perform well on real-world data. Adversarial learning for deep neural networks (DNNs) has emerged as a promising approach to improving generalization ability, particularly for image classification. In this paper, we implement a specific adversarial learning technique known as Adversarial Discriminative Domain Adaptation (ADDA) and replicate digit classification experiments from the original ADDA paper. We extend their findings by examining a broader range of domain shifts and provide a detailed analysis of in-domain classification accuracy post-ADDA. Our results demonstrate that ADDA significantly improves accuracy across certain domain shifts with minimal impact on in-domain performance. Furthermore, we provide qualitative analysis and propose potential explanations for ADDA's limitations in less successful domain shifts. Code is at https://github.com/eugenechoi2004/COS429_FINAL .

Wonjun Lee, Doehyeon Lee, Eugene Choi et al.

Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for VLMs primarily rely on automated evaluation methods, but these methods struggle to detect implicit harmful content or produce inaccurate evaluations. Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image-text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator. The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images. We filter out ambiguous and low-quality image-text pairs from existing benchmarks using the ELITE evaluator and generate diverse combinations of safe and unsafe image-text pairs. Our experiments demonstrate that the ELITE evaluator achieves superior alignment with human evaluations compared to prior automated methods, and the ELITE benchmark offers enhanced benchmark quality and diversity. By introducing ELITE, we pave the way for safer, more robust VLMs, contributing essential tools for evaluating and mitigating safety risks in real-world applications.