Summary of Mixer Is More Than Just a Model, by Qingfeng Ji et al.
Mixer is more than just a model
by Qingfeng Ji, Yuxin Wang, Letong Sun
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 Recently, MLP structures have seen a resurgence in popularity, with MLP-Mixer standing out as a prominent example. In computer vision, MLP-Mixer is notable for its ability to extract information from both channel and token perspectives, effectively fusing these two types of data. This “mixing” approach represents a paradigm shift in neural network architectures. The study explores the application of this concept in audio recognition, introducing the Audio Spectrogram Mixer with Roll-Time and Hermit FFT (ASM-RH) model that incorporates insights from both time and frequency domains. Experimental results show that ASM-RH is well-suited for audio data and achieves promising outcomes across multiple classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how neural networks can work better by combining information in different ways. One way this happens is through a model called MLP-Mixer, which takes information from both channels (like color) and tokens (like shapes). This helps the network understand things better. The researchers took this idea and applied it to audio recognition, creating a new model called ASM-RH that uses information from time and frequency domains. They tested it on different tasks and found that it did well. |
Keywords
* Artificial intelligence * Classification * Neural network * Token
