Quantifying Shape and Texture Biases for Enhancing Transfer Learning in Convolutional Neural Networks
Neural networks have inductive biases owing to the assumptions associated with the selected learning algorithm, datasets, and network structure. Specifically, convolutional neural networks (CNNs) are known for their tendency to exhibit textural biases. This bias is closely related to image classific...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
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| Series: | Signals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-6120/5/4/40 |
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