Summary of Luma: a Benchmark Dataset For Learning From Uncertain and Multimodal Data, by Grigor Bezirganyan et al.
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal Data
by Grigor Bezirganyan, Sana Sellami, Laure Berti-Équille, Sébastien Fournier
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 LUMA dataset is a unique benchmark for learning from uncertain and multimodal data, extending the well-known CIFAR 10/100 dataset with audio samples and text data generated using the Gemma-7B Large Language Model. The dataset features 50 classes of audio, image, and textual data, allowing for controlled injection of uncertainty to achieve specific experiments and benchmarking initiatives. A Python package is also provided, including functions for generating variants of the dataset, adding out-of-distribution samples, and providing a baseline pre-trained model. The LUMA dataset is intended to promote trustworthy and robust multimodal deep learning approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LUMA is a special kind of data that helps make machine learning models more reliable. It has pictures, sounds, and text from 50 different categories. This data is important because it allows researchers to test how well their models work when there’s uncertainty or noise in the input. The dataset comes with tools that can generate different versions of the data and add extra challenges for the model to learn. This will help developers create more trustworthy machine learning systems. |
Keywords
» Artificial intelligence » Deep learning » Large language model » Machine learning
