Summary of Enhancing Continuous Domain Adaptation with Multi-path Transfer Curriculum, by Hanbing Liu et al.
Enhancing Continuous Domain Adaptation with Multi-Path Transfer Curriculum
by Hanbing Liu, Jingge Wang, Xuan Zhang, Ye Guo, Yang Li
First submitted to arxiv on: 26 Feb 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 novel Continuous Domain Adaptation (CDA) method, W-MPOT, addresses the large distribution gap between training and testing data by utilizing a series of intermediate domains. The approach constructs a transfer curriculum based on Wasserstein distance, motivated by theoretical analysis of CDA. It then transfers the source model to the target domain through multiple valid paths in the curriculum using continuous optimal transport with bidirectional path consistency constraints to mitigate accumulated mapping errors. W-MPOT achieves up to 54.1% accuracy improvement on multi-session Alzheimer MR image classification and 94.7% MSE reduction on battery capacity estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary W-MPOT is a new way to help machines learn from one data set, but be good at another. Right now, this gap between training and testing data is a big problem in machine learning. W-MPOT uses many intermediate steps to get better results. It’s like a step-by-step guide that helps the machine understand how to transfer its knowledge from one domain to another. This approach works really well on some datasets, getting up to 54% more accurate or reducing errors by almost 95%. |
Keywords
* Artificial intelligence * Domain adaptation * Image classification * Machine learning * Mse
