Autoencoders for sample size estimation for fully connected neural network classifiers.

ABSTRACT: Sample size estimation is a crucial step in experimental design but is understudied in the context of deep learning. Currently, estimating the quantity of labeled data needed to train a classifier to a desired performance, is largely based on prior experience with similar models and problems or on untested heuristics. In many supervised machine learning applications, data labeling can be expensive and time-consuming and would benefit from a more rigorous means of estimating labeling requirements. Here, we study the problem of estimating the minimum sample size of labeled training data necessary for training computer vision models as an exemplar for other deep learning problems. We consider the problem of identifying the minimal number of labeled data points to achieve a generalizable repre

SUBMITTER: Gulamali FF 

PROVIDER: S-EPMC9747810 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature