Summary of Bag Of Tricks For Multimodal Automl with Image, Text, and Tabular Data, by Zhiqiang Tang et al.
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data
by Zhiqiang Tang, Zihan Zhong, Tong He, Gerald Friedland
First submitted to arxiv on: 19 Dec 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 A novel study explores best practices for automatic machine learning (AutoML) in multimodal settings, where data combines images, text, and tabular information. The research focuses on classification and regression tasks involving flexible combinations of these modalities. A comprehensive benchmark is created, comprising 22 datasets from various real-world applications. The study scrutinizes design choices related to multimodal fusion strategies, data augmentation, converting tabular data into text, cross-modal alignment, and handling missing modalities. Through extensive experimentation and analysis, the researchers distill a collection of effective strategies and present a unified pipeline achieving robust performance on diverse datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make machines learn better when they have different types of information, like pictures, words, and numbers. Right now, most machine learning programs only work well with one type of data at a time. But what if we want to use all these different kinds of information together? The researchers in this study try to figure out the best way to do this by looking at 22 different datasets that combine images, text, and numbers from real-world applications. They find some important strategies that work well for combining these different types of data, and they show how these strategies can be used together to get good results. |
Keywords
» Artificial intelligence » Alignment » Classification » Data augmentation » Machine learning » Regression
