Uncertainty-Inclusive Contrastive Learning for Leveraging Synthetic Images
Recent advancements in text-to-image generation models have sparked a growing interest in using synthesized training data to improve few-shot learning performance. Prevailing approaches treat all generated data as uniformly important, neglecting the fact that the quality of generated images varies a...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/157230 |