The dissertation shows that text-centric prompting, inference-time optimization, and correct-by-construction synthetic data can improve LLM robustness and Verilog code generation under resource constraints.
A Study in Dataset Pruning for Image Super-Resolution
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abstract
In image Super-Resolution (SR), relying on large datasets for training is a double-edged sword. While offering rich training material, they also demand substantial computational and storage resources. In this work, we analyze dataset pruning to solve these challenges. We introduce a novel approach that reduces a dataset to a core-set of training samples, selected based on their loss values as determined by a simple pre-trained SR model. By focusing the training on just 50\% of the original dataset, specifically on the samples characterized by the highest loss values, we achieve results comparable to or surpassing those obtained from training on the entire dataset. Interestingly, our analysis reveals that the top 5\% of samples with the highest loss values negatively affect the training process. Excluding these samples and adjusting the selection to favor easier samples further enhances training outcomes. Our work opens new perspectives to the untapped potential of dataset pruning in image SR. It suggests that careful selection of training data based on loss-value metrics can lead to better SR models, challenging the conventional wisdom that more data inevitably leads to better performance.
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cs.LG 1years
2025 1verdicts
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Generalizing Large Language Model Usability Across Resource-Constrained
The dissertation shows that text-centric prompting, inference-time optimization, and correct-by-construction synthetic data can improve LLM robustness and Verilog code generation under resource constraints.