Summary of Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay, by Mohammad Rostami
Continuous Unsupervised Domain Adaptation Using Stabilized Representations and Experience Replay
by Mohammad Rostami
First submitted to arxiv on: 31 Jan 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 In this paper, researchers introduce a novel algorithm for unsupervised domain adaptation in continual learning scenarios. The goal is to maintain model generalization when new domains arrive continually without labeled data. Unlike existing UDA algorithms, which require simultaneous access to source and target datasets, or CL approaches that rely on labeled data, the proposed solution updates a base model using only unlabeled data from subsequent tasks. The algorithm stabilizes the learned internal distribution by modeling network responses in hidden layers with a Gaussian mixture model (GMM) and updating the model to match the internally learned distribution of new domains. Additionally, experience replay is used to overcome catastrophic forgetting. Theoretical analysis and comparative experiments demonstrate the effectiveness of this approach on four benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way for machines to learn about different kinds of data without labeled information. When new types of data come along, it’s hard for machines to keep learning from them while still remembering what they learned before. The researchers created an algorithm that helps machines remember and adapt to new data types without needing labels. They used a special model called Gaussian mixture model (GMM) to help the machine understand the patterns in the data. This approach is useful because it can learn about many different kinds of data without needing labeled examples. |
Keywords
* Artificial intelligence * Continual learning * Domain adaptation * Generalization * Mixture model * Unsupervised
