Efficient few-shot machine learning for classification of EBSD patterns.

ABSTRACT: Deep learning is quickly becoming a standard approach to solving a range of materials science objectives, particularly in the field of computer vision. However, labeled datasets large enough to train neural networks from scratch can be challenging to collect. One approach to accelerating the training of deep learning models such as convolutional neural networks is the transfer of weights from models trained on unrelated image classification problems, commonly referred to as transfer learning. The powerful feature extractors learned previously can potentially be fine-tuned for a new classification problem without hindering performance. Transfer learning can also improve the results of training a model using a small amount of data, known as few-shot learning. Herein, we test the effectivenes

SUBMITTER: Kaufmann K 

PROVIDER: S-EPMC8046977 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature