Tiny models from tiny data: Textual and null-text inversion for few-shot distillation
This work addresses the challenge of deploying efficient, tiny models in few-shot settings where large unlabeled datasets are unavailable, offering a practical solution for resource-constrained applications.
The paper tackles the problem of few-shot learning for image classification by proposing a diffusion model inversion technique (TINT) that combines textual and null-text inversion to generate synthetic data for knowledge distillation, achieving state-of-the-art accuracy among small student models on benchmarks while being faster than prior work.
Few-shot learning deals with problems such as image classification using very few training examples. Recent vision foundation models show excellent few-shot transfer abilities, but are large and slow at inference. Using knowledge distillation, the capabilities of high-performing but slow models can be transferred to tiny, efficient models. However, common distillation methods require a large set of unlabeled data, which is not available in the few-shot setting. To overcome this lack of data, there has been a recent interest in using synthetic data. We expand on this line of research by presenting a novel diffusion model inversion technique (TINT) combining the diversity of textual inversion with the specificity of null-text inversion. Using this method in a few-shot distillation pipeline leads to state-of-the-art accuracy among small student models on popular benchmarks, while being significantly faster than prior work. Popular few-shot benchmarks involve evaluation over a large number of episodes, which is computationally cumbersome for methods involving synthetic data generation. We also present a theoretical analysis on how the accuracy estimator variance depends on the number of episodes and query examples, and use these results to lower the computational effort required for method evaluation. Finally, to further motivate the use of generative models in few-shot distillation, we demonstrate that our method outperforms training on real data mined from the dataset used in the original diffusion model training. Source code is available at https://github.com/pixwse/tiny2.