Summary of Bayesian Learning-driven Prototypical Contrastive Loss For Class-incremental Learning, by Nisha L. Raichur et al.
Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning
by Nisha L. Raichur, Lucas Heublein, Tobias Feigl, Alexander Rügamer, Christopher Mutschler, Felix Ott
First submitted to arxiv on: 17 May 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 The proposed prototypical network with Bayesian learning-driven contrastive loss (BLCL) enables optimal representation learning between previous class prototypes and newly encountered ones in class-incremental learning scenarios. This approach dynamically adapts the balance between cross-entropy and contrastive loss functions using a Bayesian learning technique, reducing intra-class distance and increasing inter-class distance. Experimental evaluations on CIFAR-10 and CIFAR-100 for image classification and GNSS-based dataset for interference classification demonstrate the effectiveness of this method, outperforming existing state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to learn from data that is constantly changing, like when you’re learning new things over time. They created a special network that helps computers remember what they learned before and adapt to new information. This approach makes sure the computer doesn’t forget what it already knew, which is a problem called “catastrophic forgetting”. The team tested their method on images of different objects and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Classification » Contrastive loss » Cross entropy » Image classification » Representation learning
