Jinliang Cong

Suhang Gu, Ye Wang, Yongxin Chou et al.

Clustering is an efficient and essential technique for exploring latent knowledge of data. However, limited attention has been given to the interpretability of the clusters detected by most clustering algorithms. In addition, due to the homogeneity of data, different groups of data have their own homogeneous styles. In this paper, the above two aspects are considered, and an interpretable style Takagi-Sugeno-Kang (TSK) fuzzy clustering (IS-TSK-FC) algorithm is proposed. The clustering behavior of IS-TSK-FC is fully guided by the TSK fuzzy inference on fuzzy rules. In particular, samples are grouped into clusters represented by the corresponding consequent vectors of all fuzzy rules learned in an unsupervised manner. This can explain how the clusters are generated in detail, thus making the underlying decision-making process of the IS-TSK-FC interpretable. Moreover, a series of style matrices are introduced to facilitate the consequents of fuzzy rules in IS-TSK-FC by capturing the styles of clusters as well as the nuances between different styles. Consequently, all the fuzzy rules in IS-TSK-FC have powerful data representation capability. After determining the antecedents of all the fuzzy rules, the optimization problem of IS-TSK-FC can be iteratively solved in an alternation manner. The effectiveness of IS-TSK-FC as an interpretable clustering tool is validated through extensive experiments on benchmark datasets with unknown implicit/explicit styles. Specially, the superior clustering performance of IS-TSK-FC is demonstrated on case studies where different groups of data present explicit styles. The source code of IS-TSK-FC can be downloaded from https://github.com/gusuhang10/IS-TSK-FC.

Zhiqiang Wu, Zhaomang Sun, Tong Zhou et al.

Denoising Diffusion Probabilistic Models (DDPM) and Flow Matching (FM) generative models show promising potential for one-step Real-World Image Super-Resolution (Real-ISR). Recent one-step Real-ISR models typically inject a Low-Quality (LQ) image latent distribution at the initial timestep. However, a fundamental gap exists between the LQ image latent distribution and the Gaussian noisy latent distribution, limiting the effective utilization of generative priors. We observe that the noisy latent distribution at DDPM/FM mid-timesteps aligns more closely with the LQ image latent distribution. Based on this insight, we present One Mid-timestep Guidance Real-ISR (OMGSR), a universal framework applicable to DDPM/FM-based generative models. OMGSR injects the LQ image latent distribution at a pre-computed mid-timestep, incorporating the proposed Latent Distribution Refinement loss to alleviate the latent distribution gap. We also design the Overlap-Chunked LPIPS/GAN loss to eliminate checkerboard artifacts in image generation. Within this framework, we instantiate OMGSR for DDPM/FM-based generative models with two variants: OMGSR-S (SD-Turbo) and OMGSR-F (FLUX.1-dev). Experimental results demonstrate that OMGSR-S/F achieves balanced/excellent performance across quantitative and qualitative metrics at 512-resolution. Notably, OMGSR-F establishes overwhelming dominance in all reference metrics. We further train a 1k-resolution OMGSR-F to match the default resolution of FLUX.1-dev, which yields excellent results, especially in the details of the image generation. We also generate 2k-resolution images by the 1k-resolution OMGSR-F using our two-stage Tiled VAE & Diffusion.