Deep-Learning Core
Developing core deep learning methods for representation learning, model design, and robust visual reasoning.
Deep Learning
Projects
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Publications
A Markovian View of Iterative-Feedback Loops in Image Generative Models: Neural Resonance and Model Collapse
2026
Vibhas Kumar Vats, David Crandall, Samuel Goree • ArXiv-preprint
This paper investigates why generative models can degrade when they are repeatedly trained on AI-generated data. We introduce the idea of neural resonance, where iterative feedback pushes models toward a low-dimensional latent structure, and show the conditions under which this happens. We also analyze several model families and describe common patterns of collapse that can help guide future mitigation strategies.
DiffusionGenerative ModelsDeep LearningNeural Resonance
Controlling the Quality of Distillation in Response-Based Network Compression
2021
Vibhas Kumar Vats, David Crandall • AAAI - workshop
This paper examines why some teacher models transfer knowledge more effectively than others in knowledge distillation. We find that the teacher’s training process strongly influences how much useful class relationship information is available for the student to learn. The study provides practical guidance for training teacher models to achieve better distillation results.
Deep LearningDistillation