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eess.IV2024
Conditional Hallucinations for Image Compression
Till Aczel, Roger Wattenhofer
In lossy image compression, models face the challenge of either hallucinating details or generating out-of-distribution samples due to the information bottleneck. This implies that…
cs.LG2024
SUPClust: Active Learning at the Boundaries
Yuta Ono, Till Aczel, Benjamin Estermann +1
Active learning is a machine learning paradigm designed to optimize model performance in a setting where labeled data is expensive to acquire. In this work, we propose a novel acti…
cs.LG2024
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
Paul Doucet, Benjamin Estermann, Till Aczel +1
This study addresses the integration of diversity-based and uncertainty-based sampling strategies in active learning, particularly within the context of self-supervised pre-trained…