Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion
This work addresses the domain gap in zero-shot classification for researchers and practitioners by offering an incremental improvement over existing diffusion-based methods.
The paper tackles the problem of Model-Agnostic Zero-Shot Classification (MA-ZSC) by improving synthetic image diversity using modifications to a pre-trained diffusion model, achieving results comparable to state-of-the-art models like CLIP on datasets such as CIFAR10, CIFAR100, and EuroSAT.
In this work, we investigate the problem of Model-Agnostic Zero-Shot Classification (MA-ZSC), which refers to training non-specific classification architectures (downstream models) to classify real images without using any real images during training. Recent research has demonstrated that generating synthetic training images using diffusion models provides a potential solution to address MA-ZSC. However, the performance of this approach currently falls short of that achieved by large-scale vision-language models. One possible explanation is a potential significant domain gap between synthetic and real images. Our work offers a fresh perspective on the problem by providing initial insights that MA-ZSC performance can be improved by improving the diversity of images in the generated dataset. We propose a set of modifications to the text-to-image generation process using a pre-trained diffusion model to enhance diversity, which we refer to as our $\textbf{bag of tricks}$. Our approach shows notable improvements in various classification architectures, with results comparable to state-of-the-art models such as CLIP. To validate our approach, we conduct experiments on CIFAR10, CIFAR100, and EuroSAT, which is particularly difficult for zero-shot classification due to its satellite image domain. We evaluate our approach with five classification architectures, including ResNet and ViT. Our findings provide initial insights into the problem of MA-ZSC using diffusion models. All code will be available on GitHub.