Summary of Continual Learning with Task Specialist, by Indu Solomon et al.
Continual learning with task specialist
by Indu Solomon, Aye Phyu Phyu Aung, Uttam Kumar, Senthilnath Jayavelu
First submitted to arxiv on: 26 Sep 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 The paper proposes Continual Learning with Task Specialists (CLTS) to address catastrophic forgetting and limited labelled data in real-world datasets. The CLTS model consists of Task Specialists (T S) and Task Predictor (T P ) with a pre-trained Stable Diffusion (SD) module. It introduces a new specialist to handle a new task sequence, using variational autoencoder (V AE), K-Means block, and Bootstrapping Language-Image Pre-training (BLIP ) model for data clustering and caption generation. The proposed model generates task samples from the pre-trained SD model and trains the T P module without storing any task samples. The paper compares CLTS with four state-of-the-art models on three real-world datasets, showing that CLTS outperforms all baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for machines to learn and adapt to new information while keeping what they already know. This is called continual learning. The problem is that old knowledge can be forgotten when new knowledge comes in. The proposed model uses something called task specialists to handle this process. It also uses a type of language processing to generate captions from the input data, which helps with training. |
Keywords
» Artificial intelligence » Bootstrapping » Clustering » Continual learning » Diffusion » K means » Variational autoencoder
