Summary of A Novel Adaptive Fine-tuning Algorithm For Multimodal Models: Self-optimizing Classification and Selection Of High-quality Datasets in Remote Sensing, by Yi Ren et al.
A Novel Adaptive Fine-Tuning Algorithm for Multimodal Models: Self-Optimizing Classification and Selection of High-Quality Datasets in Remote Sensing
by Yi Ren, Tianyi Zhang, Zhixiong Han, Weibin Li, Zhiyang Wang, Wenbo Ji, Chenhao Qin, Chenbin Liang, Licheng Jiao
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 adaptive fine-tuning algorithm is proposed for multimodal large models, comprising two stages of truncation. The first stage projects vast amounts of data into a semantic vector space using MiniBatchKMeans for automated clustering, ensuring high semantic similarity within each cluster. The second stage processes the data in each cluster by calculating translational differences between original and perturbed data in the model’s vector space, serving as a generalization metric. This metric is used to select data with high generalization potential for training. The algorithm is applied to train the InternLM-XComposer2-VL-7B model on the GeoChat multimodal remote sensing dataset, outperforming state-of-the-art baselines and achieving comparable performance to the full dataset while reducing training time by 68.2%. The trained model also excels on various evaluation datasets, including UCMerced, AID, and LRBEN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to improve big models that handle many types of data is being explored. This approach has two main steps: first, it groups similar pieces of data together based on their meaning, and then it selects the most useful pieces for training. The researchers tested this method by applying it to a large model and using only one-third of the available data. They found that this reduced the time needed to train the model without sacrificing its ability to perform well on various tasks. |
Keywords
» Artificial intelligence » Clustering » Fine tuning » Generalization » Vector space
