T-former: An Efficient Transformer for Image Inpainting

Benefiting from powerful convolutional neural networks (CNNs), learning-based image inpainting methods have made significant breakthroughs over the years. However, some nature of CNNs (e.g. local prior, spatially shared parameters) limit the performance in the face of broken images with diverse and complex forms. Recently, a class of attention-based network architectures, called transformer, has shown significant performance on natural language processing fields and high-level vision tasks. Compared with CNNs, attention operators are better at long-range modeling and have dynamic weights, but their computational complexity is quadratic in spatial resolution, and thus less suitable for applications involving higher resolution images, such as image inpainting. In this paper, we design a novel attention linearly related to the resolution according to Taylor expansion. And based on this attention, a network called $T$-former is designed for image inpainting. Experiments on several benchmark datasets demonstrate that our proposed method achieves state-of-the-art accuracy while maintaining a relatively low number of parameters and computational complexity. The code can be found at \href{https://github.com/dengyecode/T-former_image_inpainting}{github.com/dengyecode/T-former\_image\_inpainting}

Auxiliary Loss Adaptation for Image Inpainting

Auxiliary losses commonly used in image inpainting lead to better reconstruction performance by incorporating prior knowledge of missing regions. However, it usually requires a lot of effort to fully exploit the potential of auxiliary losses, or otherwise, improperly weighted auxiliary losses would distract the model from the inpainting task, and the effectiveness of an auxiliary loss might vary during the training process. Hence the design of auxiliary losses takes strong domain expertise. To mitigate the problem, in this work, we introduce the Auxiliary Loss Adaptation for Image Inpainting (ALA) algorithm to dynamically adjust the parameters of the auxiliary loss. Our method is based on the principle that the best auxiliary loss is the one that helps increase the performance of the main loss most through several steps of gradient descent. We then examined two commonly used auxiliary losses in inpainting and used ALA to adapt their parameters. Experimental results show that ALA induces more competitive inpainting results than fixed auxiliary losses. In particular, simply combining auxiliary loss with ALA, existing inpainting methods can achieve increased performances without explicitly incorporating delicate network design or structure knowledge prior.

Auxiliary Loss Adaption for Image Inpainting

Auxiliary losses commonly used in image inpainting lead to better reconstruction performance by incorporating prior knowledge of missing regions. However, it usually takes a lot of effort to fully exploit the potential of auxiliary losses, since improperly weighted auxiliary losses would distract the model from the inpainting task, and the effectiveness of an auxiliary loss might vary during the training process. Furthermore, the design of auxiliary losses takes domain expertise. In this work, we introduce the Auxiliary Loss Adaption (Adaption) algorithm to dynamically adjust the parameters of the auxiliary loss, to better assist the primary task. Our algorithm is based on the principle that better auxiliary loss is the one that helps increase the performance of the main loss through several steps of gradient descent. We then examined two commonly used auxiliary losses in inpainting and use \ac{ALA} to adapt their parameters. Experimental results show that ALA induces more competitive inpainting results than fixed auxiliary losses. In particular, simply combining auxiliary loss with \ac{ALA}, existing inpainting methods can achieve increased performances without explicitly incorporating delicate network design or structure knowledge prior.

Distributed Generative Adversarial Net

Recently the Generative Adversarial Network has become a hot topic. Considering the application of GAN in multi-user environment, we propose Distributed-GAN. It enables multiple users to train with their own data locally and generates more diverse samples. Users don't need to share data with each other to avoid the leakage of privacy. In recent years, commercial companies have launched cloud platforms based on artificial intelligence to provide model for users who lack computing power. We hope our work can inspire these companies to provide more powerful AI services.