Summary of Multi-head Encoding For Extreme Label Classification, by Daojun Liang et al.
Multi-Head Encoding for Extreme Label Classification
by Daojun Liang, Haixia Zhang, Dongfeng Yuan, Minggao Zhang
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Multi-Head Encoding (MHE) mechanism addresses the Classifier Computational Overload Problem (CCOP) in eXtreme Label Classification (XLC) by decomposing extreme labels into short local labels. This reduces the computational load geometrically, allowing for faster training and inference processes. The MHE-based implementations, including Multi-Head Product, Multi-Head Cascade, and Multi-Head Sampling, are designed to effectively cope with CCOP in different XLC tasks, such as single-label, multi-label, and model pretraining tasks. Experimental results show that the proposed methods achieve state-of-the-art performance while significantly streamlining the training and inference processes of XLC tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary XLC is a way for machine learning to work with lots of categories and labels in real-world data. However, as the number of categories grows, the model gets slower and harder to use. The researchers propose a new technique called Multi-Head Encoding (MHE) that solves this problem by breaking down complex labels into smaller, simpler ones. This makes the model much faster and easier to train. They also show that their method can perform just as well as more complicated models while being much faster. The code for this project is publicly available. |
Keywords
» Artificial intelligence » Classification » Inference » Machine learning » Pretraining
