Summary of Hypermm : Robust Multimodal Learning with Varying-sized Inputs, by Hava Chaptoukaev et al.
HyperMM : Robust Multimodal Learning with Varying-sized Inputs
by Hava Chaptoukaev, Vincenzo Marcianó, Francesco Galati, Maria A. Zuluaga
First submitted to arxiv on: 30 Jul 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 paper proposes a novel framework called HyperMM for multimodal learning (MML) with missing imaging modalities. The existing solutions for addressing missing modalities rely on modality imputation strategies, which can be computationally costly and impact subsequent prediction models. Instead, HyperMM uses an end-to-end approach to learn from varying-sized inputs. The authors introduce a conditional hypernetwork for training a universal feature extractor and a permutation-invariant neural network for processing the extracted features. They demonstrate the advantages of their method in two tasks: Alzheimer’s disease detection and breast cancer classification. The results show that HyperMM is robust to high rates of missing data and can handle varying-sized datasets beyond the scenario of missing modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to combine different types of information, called multimodal learning. It’s like trying to solve a puzzle with many pieces that are all connected in some way. When we have all the pieces, it helps us make better decisions and predictions. But what if some of the pieces are missing? Most methods try to fill in the gaps first, but this can be time-consuming and affect the final result. This paper proposes a new approach called HyperMM that can learn from incomplete information directly. They tested their method on two important tasks: detecting Alzheimer’s disease and diagnosing breast cancer. The results show that HyperMM works well even when some of the data is missing, making it useful for real-world applications. |
Keywords
» Artificial intelligence » Classification » Neural network
