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About 300 PDF
This document discusses and compares multiple data augmentation techniques for image classification using deep learning models. It explores using traditional transformations like cropping and flipping images, GANs to generate new image styles, and allowing neural networks to learn augmentations that improve classifier performance directly. The techniques are evaluated on small subsets of ImageNet and MNIST datasets and aim to address the common problem of limited training data hindering effective model training.
Detailed Information
| Author: | ['chloe trump'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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