Summary of Long-tailed Recognition on Binary Networks by Calibrating a Pre-trained Model, By Jihun Kim et al.
Long-Tailed Recognition on Binary Networks by Calibrating A Pre-trained Model
by Jihun Kim, Dahyun Kim, Hyungrok Jung, Taeil Oh, Jonghyun Choi
First submitted to arxiv on: 30 Mar 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 This paper proposes a novel approach to deploying deep models in real-world scenarios, specifically addressing the challenges of computational efficiency and learning from long-tailed data distributions. The authors develop a calibrate-and-distill framework that leverages off-the-shelf pretrained full-precision models as teachers for distilling knowledge into binary neural networks trained on long-tailed datasets. To improve generalization capabilities, the researchers also introduce an adversarial balancing mechanism among terms in the objective function and an efficient multiresolution learning scheme. The proposed method is evaluated using 15 datasets, including newly derived long-tailed datasets from existing balanced datasets, demonstrating significant improvements over prior art (average margin: >14.33%). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in artificial intelligence. Right now, it’s hard to use powerful AI models in real-life situations because they need too much computer power and are trained on data that isn’t very representative of the world. The authors created a new way to teach these models using smaller “binary” versions that can be trained quickly and accurately even with limited data. They also came up with two important tricks: one helps the model learn from different types of data, and the other makes sure it doesn’t get stuck in a rut by trying lots of different ideas. To test their idea, they used 15 different datasets and showed that their method is much better than previous approaches. |
Keywords
» Artificial intelligence » Generalization » Objective function » Precision
