HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks

Editing real images authentically while also achieving cross-domain editing remains a challenge. Recent studies have focused on converting real images into latent codes and accomplishing image editing by manipulating these codes. However, merely manipulating the latent codes would constrain the edited images to the generator's image domain, hindering the attainment of diverse editing goals. In response, we propose an innovative image editing method called HyperEditor, which utilizes weight factors generated by hypernetworks to reassign the weights of the pre-trained StyleGAN2's generator. Guided by CLIP's cross-modal image-text semantic alignment, this innovative approach enables us to simultaneously accomplish authentic attribute editing and cross-domain style transfer, a capability not realized in previous methods. Additionally, we ascertain that modifying only the weights of specific layers in the generator can yield an equivalent editing result. Therefore, we introduce an adaptive layer selector, enabling our hypernetworks to autonomously identify the layers requiring output weight factors, which can further improve our hypernetworks' efficiency. Extensive experiments on abundant challenging datasets demonstrate the effectiveness of our method.

When Source-Free Domain Adaptation Meets Label Propagation

Source-free domain adaptation, where only a pre-trained source model is used to adapt to the target distribution, is a more general approach to achieving domain adaptation. However, it can be challenging to capture the inherent structure of the target features accurately due to the lack of supervised information on the target domain. To tackle this problem, we propose a novel approach called Adaptive Local Transfer (ALT) that tries to achieve efficient feature clustering from the perspective of label propagation. ALT divides the target data into inner and outlier samples based on the adaptive threshold of the learning state, and applies a customized learning strategy to best fits the data property. Specifically, inner samples are utilized for learning intra-class structure thanks to their relatively well-clustered properties. The low-density outlier samples are regularized by input consistency to achieve high accuracy with respect to the ground truth labels. In this way, local clustering can be prevented from forming spurious clusters while effectively propagating label information among subpopulations. Empirical evidence demonstrates that ALT outperforms the state of the arts on three public benchmarks: Office-31, Office-Home, and VisDA.