Summary of Self-supervised Quantization-aware Knowledge Distillation, by Kaiqi Zhao et al.
Self-Supervised Quantization-Aware Knowledge Distillation
by Kaiqi Zhao, Ming Zhao
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 framework called Self-Supervised Quantization-Aware Knowledge Distillation (SQAKD) that combines quantization-aware training (QAT) and knowledge distillation (KD) to achieve competitive performance in creating low-bit deep learning models. SQAKD unifies the forward and backward dynamics of various quantization functions, making it flexible for incorporating different QAT works. The framework formulates QAT as a co-optimization problem that simultaneously minimizes the KL-Loss between full-precision and low-bit models for KD and the discretization error for quantization, without supervision from labels. This approach outperforms state-of-the-art QAT and KD works for various model architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers understand things better by using special techniques to train models that use less computer memory. It’s like teaching a student how to simplify complex ideas into simpler ones, so they can learn faster and more efficiently. The new method, called SQAKD, makes it easier to create these simplified models by combining two older approaches: quantization-aware training (QAT) and knowledge distillation (KD). This way, computers can use less energy and memory while still being able to understand things correctly. |
Keywords
» Artificial intelligence » Deep learning » Knowledge distillation » Optimization » Precision » Quantization » Self supervised
