HyeKyung Yoon

Minhyuk Lee, Hyekyung Yoon, Myungjoo Kang

Text-to-Image (T2I) diffusion models enable high quality open ended synthesis, but practical use requires suppressing unsafe generations while preserving behavior on benign prompts. We study this tension relative to the frozen generator, using its prompt conditioned distribution as the preservation reference. Since T2I safety is commonly evaluated by bounded risk scores on generated images, total variation (TV) bounds how much expected risk can change from this reference. We call this fixed reference constraint the Safety-Prompt Alignment Tradeoff (SPAT): reducing expected unsafety requires prompt conditioned distributional deviation. To make this deviation selective and adjustable, we define the tau safe set as prompts whose reference risk is at most tau, and cast intervention as projection toward nearby prompts in this set. We propose Selective Prompt prOjecTion (SPOT), an inference time framework that approximates this projection without retraining the generator or learning a category specific rewriter. SPOT uses an LLM to rank candidate rewrites and a safeguard VLM to accept generated images under the same tau. Across four datasets and three diffusion backbones, SPOT achieves relative inappropriate (IP) score reductions from 14.2% to 44.4% over strong safety alignment baselines while keeping benign prompt behavior close to the fixed reference.

Minhyuk Lee, HyeKyung Yoon, MyungJoo Kang

Multivariate long-term time series forecasting is critical for applications such as weather prediction, and traffic analysis. In addition, the implementation of Transformer variants has improved prediction accuracy. Following these variants, different input data process approaches also enhanced the field, such as tokenization techniques including point-wise, channel-wise, and patch-wise tokenization. However, previous studies still have limitations in time complexity, computational resources, and cross-dimensional interactions. To address these limitations, we introduce a novel CNN Autoencoder-based Score Attention mechanism (CASA), which can be introduced in diverse Transformers model-agnosticically by reducing memory and leading to improvement in model performance. Experiments on eight real-world datasets validate that CASA decreases computational resources by up to 77.7%, accelerates inference by 44.0%, and achieves state-of-the-art performance, ranking first in 87.5% of evaluated metrics.