Summary of Clip-embed-kd: Computationally Efficient Knowledge Distillation Using Embeddings As Teachers, by Lakshmi Nair
CLIP-Embed-KD: Computationally Efficient Knowledge Distillation Using Embeddings as Teachers
by Lakshmi Nair
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 extends Contrastive Language-Image Pre-training (CLIP) for efficient knowledge distillation by utilizing embeddings as teachers. The authors show that using only the embeddings of a teacher model can significantly reduce computational requirements while achieving comparable performance to full-scale knowledge distillation. Their preliminary results demonstrate that CLIP-based knowledge distillation with embeddings outperforms traditional methods, requiring 9 times less memory and 8 times less training time. This work has significant implications for large-scale language and vision models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a big discovery about how to train artificial intelligence (AI) models using pictures and words. Right now, AI models are very good at recognizing things in pictures or understanding what people say, but they often struggle when trying to do both tasks together. The researchers found a way to make the AI models better at this by using a technique called “contrastive language-image pre-training” (CLIP). They also figured out how to make this process more efficient by only using certain parts of the model instead of running the whole thing. This is important because it means we can train these AI models faster and with less memory, which makes them even more useful for things like image recognition and natural language processing. |
Keywords
* Artificial intelligence * Knowledge distillation * Natural language processing * Teacher model
