Compressing Massive Datasets with Dataset Quantization
- Amir Kidwai
- Aug 23, 2023
- 2 min read
Training advanced deep learning models demands massive datasets. But how much of this data is really needed? In a breakthrough new technique called dataset quantization (DQ), researchers show we can radically shrink datasets like ImageNet while retaining full model accuracy.
Why This Matters
With DQ, only 60% of ImageNet is required to train models like ResNet without any performance drop. For language models, 98% of instruction tuning data can be removed. This unlocks huge savings in storage, transmission and compute costs for both research and deployment.
DQ also enables training on limited resources like laptops and mobiles where running on the full dataset would be infeasible. And it opens the door to embedding model training directly on edge devices rather than the cloud.
Key Technical Achievements
DQ divides the dataset into non-overlapping bins using an optimization strategy to maximize diversity.
Samples are then drawn uniformly from each bin. This compressed dataset covers the distribution better than alternatives.
Image patches are scored by importance and lower-scoring ones removed. Images are reconstructed during training for efficiency.
In experiments, DQ consistently outperforms prior dataset compression techniques like coresets.
On ImageNet, CIFAR10 and instruction tuning tasks, DQ provides state-of-the-art compression rates.
Looking Ahead
By enabling training on far fewer examples, DQ makes it practical to embed model learning on edge devices. This could enable smarter applications while preserving privacy.
The efficiency gains also facilitate training larger models on more data. Models pre-trained on massive corpora can later be fine-tuned on compressed task datasets.
As data grows ever larger, DQ offers a crucial tool for compression. And it sets a new bar for how much we can condense datasets without performance drops. The ripple effects on costs and capabilities will be transformative.
Read paper at - https://arxiv.org/pdf/2308.10524v1.pdf
Comments