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About ResNet Presentation
This document presents the paper "Deep Residual Learning for Image Recognition" by Kaiming He et al. The paper proposes residual learning frameworks to enable the training of networks substantially deeper than those used previously. The authors found that deeper networks can be trained by explicitly letting these layers fit a residual mapping. On the ImageNet dataset, residual nets with a depth of 152 layers achieved 3.57% error on the test set. This represents a significant improvement over previous results and demonstrates the benefits of residual learning for training very deep convolutional networks.
Detailed Information
| Author: | ['Faiza'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 25 |
| Language: | English |
| Format: | |
| Price: | FREE |
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