Search Results for author:
Found 6 papers, 4 papers with code
Differences Between Hard and Noisy-labeled Samples: An Empirical Study
We study various data partitioning methods in the presence of label noise and observe that filtering out noisy samples from hard samples with this proposed metric results in the best datasets as evidenced by the high test accuracy achieved after models are trained on the filtered datasets.
Leveraging Unlabeled Data to Track Memorization
We empirically show the effectiveness of our metric in tracking memorization on various architectures and datasets and provide theoretical insights into the design of the susceptibility metric.
Disparity Between Batches as a Signal for Early Stopping
We propose a metric for evaluating the generalization ability of deep neural networks trained with mini-batch gradient descent.
Early Stopping by Gradient Disparity
Validation-based early-stopping methods are one of the most popular techniques used to avoid over-training deep neural networks.
Generalization Comparison of Deep Neural Networks via Output Sensitivity
We find a rather strong empirical relation between the output sensitivity and the variance in the bias-variance decomposition of the loss function, which hints on using sensitivity as a metric for comparing the generalization performance of networks, without requiring labeled data.
On the Reflection of Sensitivity in the Generalization Error
We find a rather strong empirical relation between the output sensitivity and the variance in the bias-variance decomposition of the loss function, which hints on using sensitivity as a metric for comparing generalization performance of networks, without requiring labeled data.
