Summary of Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss, by Kunal Dahiya et al.
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss
by Kunal Dahiya, Diego Ortego, David Jiménez
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 PRIME method tackles Extreme Multi-label Classification (XMC) by introducing a novel prototypical contrastive learning technique to balance efficiency and performance. It frames XMC as a data-to-prototype prediction task, where label prototypes aggregate information from related queries. The approach uses a shallow transformer encoder, the Label Prototype Network, which enriches label representations by combining text-based embeddings, label centroids, and learnable free vectors. A deep encoder is jointly trained with the Label Prototype Network using an adaptive triplet loss objective that adapts to high-granularity and ambiguous extreme label spaces. The method achieves state-of-the-art results in public benchmarks of different sizes and domains while maintaining efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Extreme Multi-label Classification (XMC) tries to find many relevant labels for a given text. This is a hard problem because there are so many possible labels. Recently, some smart ways have been developed to solve this issue using deep learning models. However, these methods can be slow and use a lot of computing power. In this paper, the authors propose a new method called PRIME that tries to balance speed and performance. They think about XMC as a problem where they need to find patterns in text data that help identify relevant labels. Their approach uses a simple neural network to learn from both text and label information, making it more efficient and accurate. This method performs better than other approaches on various public datasets while keeping the model easy to use. |
Keywords
» Artificial intelligence » Classification » Deep learning » Encoder » Neural network » Transformer » Triplet loss
