Chih-Fan Hsu

Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen

The absence of training data and their distribution changes in federated learning (FL) can significantly undermine model performance, especially in cross-silo scenarios. To address this challenge, we introduce the Federated Learning with Data Digest (FedDig) framework. FedDig manages unexpected distribution changes using a novel privacy-controllable data digest representation. This framework allows FL users to adjust the protection levels of the digest by manipulating hyperparameters that control the mixing of multiple low-dimensional features and applying differential privacy perturbation to these mixed features. Evaluation of FedDig across four diverse public datasets shows that it consistently outperforms five baseline algorithms by substantial margins in various data absence scenarios. We also thoroughly explored FedDig's hyperparameters, demonstrating its adaptability. Notably, the FedDig plugin design is inherently extensible and compatible with existing FL algorithms.

Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen

Continual learning (CL) seeks to mitigate catastrophic forgetting when models are trained with sequential tasks. A common approach, experience replay (ER), stores past exemplars but only sparsely approximates the data distribution, yielding fragile and oversimplified decision boundaries. We address this limitation by introducing synthetic boundary data (SBD), generated via differential privacy: inspired noise into latent features to create boundary-adjacent representations that implicitly regularize decision boundaries. Building on this idea, we propose Experience Blending (EB), a framework that jointly trains on exemplars and SBD through a dual-model aggregation strategy. EB has two components: (1) latent-space noise injection to synthesize boundary data, and (2) end-to-end training that jointly leverages exemplars and SBD. Unlike standard experience replay, SBD enriches the feature space near decision boundaries, leading to more stable and robust continual learning. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet demonstrate consistent accuracy improvements of 10%, 6%, and 13%, respectively, over strong baselines.

Jyun-Ze Tang, Chih-Fan Hsu, Jeng-Lin Li et al.

Flow matching and diffusion models have shown impressive results in text-to-image generation, producing photorealistic images through an iterative denoising process. A common strategy to speed up synthesis is to perform early denoising at lower resolutions. However, traditional methods that downscale and upscale in pixel space often introduce artifacts and distortions. These issues arise when the upscaled images are re-encoded into the latent space, leading to degraded final image quality. To address this, we propose {\bf Latent Space Scaling Generation (LSSGen)}, a framework that performs resolution scaling directly in the latent space using a lightweight latent upsampler. Without altering the Transformer or U-Net architecture, LSSGen improves both efficiency and visual quality while supporting flexible multi-resolution generation. Our comprehensive evaluation covering text-image alignment and perceptual quality shows that LSSGen significantly outperforms conventional scaling approaches. When generating $1024^2$ images at similar speeds, it achieves up to 246\% TOPIQ score improvement.

Chih-Fan Hsu, Chia-Ching Lin, Ting-Yang Hung et al.

Facial landmark detection has been studied over decades. Numerous neural network (NN)-based approaches have been proposed for detecting landmarks, especially the convolutional neural network (CNN)-based approaches. In general, CNN-based approaches can be divided into regression and heatmap approaches. However, no research systematically studies the characteristics of different approaches. In this paper, we investigate both CNN-based approaches, generalize their advantages and disadvantages, and introduce a variation of the heatmap approach, a pixel-wise classification (PWC) model. To the best of our knowledge, using the PWC model to detect facial landmarks have not been comprehensively studied. We further design a hybrid loss function and a discrimination network for strengthening the landmarks' interrelationship implied in the PWC model to improve the detection accuracy without modifying the original model architecture. Six common facial landmark datasets, AFW, Helen, LFPW, 300-W, IBUG, and COFW are adopted to train or evaluate our model. A comprehensive evaluation is conducted and the result shows that the proposed model outperforms other models in all tested datasets.