Summary of Empowering Source-free Domain Adaptation Via Mllm-guided Reliability-based Curriculum Learning, by Dongjie Chen et al.
Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning
by Dongjie Chen, Kartik Patwari, Zhengfeng Lai, Xiaoguang Zhu, Sen-ching Cheung, Chen-Nee Chuah
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Reliability-based Curriculum Learning (RCL) framework seamlessly integrates multiple Multimodal Large Language Models (MLLMs) to effectively adapt pre-trained models to target domains using only unlabeled data. By leveraging pseudo-labeling and incorporating Reliable Knowledge Transfer, Self-correcting and MLLM-guided Knowledge Expansion, and Multi-hot Masking Refinement, RCL enhances adaptability and robustness without requiring access to source data. This novel framework achieves state-of-the-art performance on multiple SFDA benchmarks, including a +9.4% improvement on DomainNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to help computers learn from one domain and apply that learning to another domain, without needing any labeled data from the target domain. They use special kinds of large language models that can understand text and images together. These models are very good at understanding visual and textual information, but they have some challenges when used in this kind of adaptation task. To overcome these issues, the researchers came up with a new framework called Reliability-based Curriculum Learning (RCL). This framework uses multiple large language models to learn from the target domain’s data and then applies what it has learned. The results show that RCL performs better than other methods on several benchmark tests. |
Keywords
» Artificial intelligence » Curriculum learning
