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Found 5 papers, 1 papers with code
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation
We propose a novel approach for SF-OSDA that exploits the granularity of target-private categories by segregating their samples into multiple unknown classes.
Towards the Reusability and Compositionality of Causal Representations
Causal Representation Learning (CRL) aims at identifying high-level causal factors and their relationships from high-dimensional observations, e. g., images.
Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis
This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical understanding of the complex relationships between multiple key design factors in SF-UDA methods.
Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation
Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks.
GANzzle: Reframing jigsaw puzzle solving as a retrieval task using a generative mental image
Puzzle solving is a combinatorial challenge due to the difficulty of matching adjacent pieces.
