Active Generation for Image Classification
Tao Huang Jiaqi Liu Shan You Chang Xu
Recently, the growing capabilities of deep generative models have underscored
their potential in enhancing image classification accuracy. However, existing
methods often demand the generation of a disproportionately large number of
images compared to the original dataset, while having only marginal
improvements in accuracy. This computationally expensive and time-consuming
process hampers the practicality of such approaches. In this paper, we propose
to address the efficiency of image generation by focusing on the specific needs
and characteristics of the model. With a central tenet of active learning, our
method, named ActGen, takes a training-aware approach to image generation. It
aims to create images akin to the challenging or misclassified samples
encountered by the current model and incorporates these generated images into
the training set to augment model performance. ActGen introduces an attentive
image guidance technique, using real images as guides during the denoising
process of a diffusion model. The model's attention on class prompt is
leveraged to ensure the preservation of similar foreground object while
diversifying the background. Furthermore, we introduce a gradient-based
generation guidance method, which employs two losses to generate more
challenging samples and prevent the generated images from being too similar to
previously generated ones. Experimental results on the CIFAR and ImageNet
datasets demonstrate that our method achieves better performance with a
significantly reduced number of generated images.