Summary of Designed Dithering Sign Activation For Binary Neural Networks, by Brayan Monroy et al.
Designed Dithering Sign Activation for Binary Neural Networks
by Brayan Monroy, Juan Estupiñan, Tatiana Gelvez-Barrera, Jorge Bacca, Henry Arguello
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel activation function is proposed in this paper to improve the performance of binary neural networks (BNNs) in computer vision tasks. The existing Sign activation function used in BNNs has a single threshold that abruptly binarizes values, leading to loss of fine-grained details. To address this issue, the authors introduce a dithering Sign activation function that applies multiple thresholds, shifting the activation level for each pixel based on spatially periodic threshold kernels. This approach takes advantage of correlations between adjacent pixels and balances detail preservation with computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new activation function is designed to work effectively in binary neural networks, improving their performance without increasing computational costs. The authors demonstrate the effectiveness of this approach through experiments on a classification task. The proposed dithering Sign activation function shows promise as an alternative activation method for BNNs. |
Keywords
» Artificial intelligence » Classification
