UniHDA: Towards Universal Hybrid Domain Adaptation of Image Generators

Generative domain adaptation has achieved remarkable progress, enabling us to adapt a pre-trained generator to a new target domain. However, existing methods simply adapt the generator to a single target domain and are limited to a single modality, either text-driven or image-driven. Moreover, they are prone to overfitting domain-specific attributes, which inevitably compromises cross-domain consistency. In this paper, we propose UniHDA, a unified and versatile framework for generative hybrid domain adaptation with multi-modal references from multiple domains. We use CLIP encoder to project multi-modal references into a unified embedding space and then linear interpolate the direction vectors from multiple target domains to achieve hybrid domain adaptation. To ensure the cross-domain consistency, we propose a novel cross-domain spatial structure (CSS) loss that maintains detailed spatial structure information between source and target generator. Experiments show that the adapted generator can synthesise realistic images with various attribute compositions. Additionally, our framework is versatile to multiple generators, \eg, StyleGAN2 and Diffusion Models.

Local Conditional Controlling for Text-to-Image Diffusion Models

Diffusion models have exhibited impressive prowess in the text-to-image task. Recent methods add image-level controls, e.g., edge and depth maps, to manipulate the generation process together with text prompts to obtain desired images. This controlling process is globally operated on the entire image, which limits the flexibility of control regions. In this paper, we introduce a new simple yet practical task setting: local control. It focuses on controlling specific local areas according to user-defined image conditions, where the rest areas are only conditioned by the original text prompt. This manner allows the users to flexibly control the image generation in a fine-grained way. However, it is non-trivial to achieve this goal. The naive manner of directly adding local conditions may lead to the local control dominance problem. To mitigate this problem, we propose a training-free method that leverages the updates of noised latents and parameters in the cross-attention map during the denosing process to promote concept generation in non-control areas. Moreover, we use feature mask constraints to mitigate the degradation of synthesized image quality caused by information differences inside and outside the local control area. Extensive experiments demonstrate that our method can synthesize high-quality images to the prompt under local control conditions. Code is available at https://github.com/YibooZhao/Local-Control.

EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification

Automatic multi-hop fact verification task has gained significant attention in recent years. Despite impressive results, these well-designed models perform poorly on out-of-domain data. One possible solution is to augment the training data with counterfactuals, which are generated by minimally altering the causal features of the original data. However, current counterfactual data augmentation techniques fail to handle multi-hop fact verification due to their incapability to preserve the complex logical relationships within multiple correlated texts. In this paper, we overcome this limitation by developing a rationale-sensitive method to generate linguistically diverse and label-flipping counterfactuals while preserving logical relationships. In specific, the diverse and fluent counterfactuals are generated via an Explain-Edit-Generate architecture. Moreover, the checking and filtering modules are proposed to regularize the counterfactual data with logical relations and flipped labels. Experimental results show that the proposed approach outperforms the SOTA baselines and can generate linguistically diverse counterfactual data without disrupting their logical relationships.

A Lightweight Algorithm to Uncover Deep Relationships in Data Tables

Many data we collect today are in tabular form, with rows as records and columns as attributes associated with each record. Understanding the structural relationship in tabular data can greatly facilitate the data science process. Traditionally, much of this relational information is stored in table schema and maintained by its creators, usually domain experts. In this paper, we develop automated methods to uncover deep relationships in a single data table without expert or domain knowledge. Our method can decompose a data table into layers of smaller tables, revealing its deep structure. The key to our approach is a computationally lightweight forward addition algorithm that we developed to recursively extract the functional dependencies between table columns that are scalable to tables with many columns. With our solution, data scientists will be provided with automatically generated, data-driven insights when exploring new data sets.