Summary of Neuro-inspired Information-theoretic Hierarchical Perception For Multimodal Learning, by Xiongye Xiao et al.
Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
by Xiongye Xiao, Gengshuo Liu, Gaurav Gupta, Defu Cao, Shixuan Li, Yaxing Li, Tianqing Fang, Mingxi Cheng, Paul Bogdan
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This research paper proposes a novel Information-Theoretic Hierarchical Perception (ITHP) model for integrating information from multiple sources or modalities in autonomous systems and cyber-physical systems. Inspired by neuroscience, the ITHP model utilizes the concept of information bottleneck to achieve compact latent state representations that retain relevant information while minimizing redundancy. Unlike traditional fusion models, ITHP designates a prime modality and treats remaining modalities as detectors in the information pathway, serving to distill the flow of information. The proposed perception model focuses on constructing an effective and compact information flow by balancing the minimization of mutual information between the latent state and input modal state, and the maximization of mutual information between the latent states and remaining modal states. Experimental evaluations on the MUStARD, CMU-MOSI, and CMU-MOSEI datasets demonstrate that ITHP consistently outperforms state-of-the-art benchmarks in multimodal representation learning, including achieving human-level performance in multimodal sentiment binary classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for machines to understand information from different sources. It’s like how our brains put together clues from our senses to understand the world. The model uses ideas from neuroscience and is good at finding important information while ignoring things that aren’t useful. This helps machines learn better when they have information from multiple sources, like pictures and sounds. The researchers tested their model on different datasets and it worked really well, even beating human-level performance in some cases. |
Keywords
» Artificial intelligence » Classification » Representation learning
