Summary of Tfdmnet: a Novel Network Structure Combines the Time Domain and Frequency Domain Features, by Hengyue Pan et al.
TFDMNet: A Novel Network Structure Combines the Time Domain and Frequency Domain Features
by Hengyue Pan, Yixin Chen, Zhiliang Tian, Peng Qiao, Linbo Qiao, Dongsheng Li
First submitted to arxiv on: 29 Jan 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 The proposed Element-wise Multiplication Layer (EML) aims to replace traditional convolutional neural networks (CNNs) in computer vision tasks. EMLs can be trained in the frequency domain, reducing computation complexity and making them more parallelizable. Theoretical analyses demonstrate the benefits of using EMLs over CNNs. To mitigate over-fitting, a Weight Fixation mechanism is introduced. Additionally, the working behavior of Batch Normalization and Dropout are analyzed in the frequency domain. A new network structure, Time-Frequency Domain Mixture Network (TFDMNet), combines the advantages of both convolution layers and EMLs. Experimental results show that TFDMNet achieves good performance on MNIST, CIFAR-10, and ImageNet databases with fewer operations compared to corresponding CNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to recognize objects in images using artificial intelligence. Currently, these systems rely on a type of neural network called convolutional neural networks (CNNs). While they work well, they require a lot of computer power and can’t be easily used on smaller devices. This research proposes a new way of doing things, called the Element-wise Multiplication Layer (EML), which is better for certain tasks and uses less energy. The scientists also came up with ways to make these systems less likely to get stuck in a rut or overfitting. They even developed a new system that combines the best of both worlds. When they tested it, they found that this new approach worked just as well on popular image recognition tasks like recognizing handwritten numbers, objects in pictures, and scenes from movies. |
