Rethinking cluster-conditioned diffusion models for label-free image synthesis
Diffusion-based image generation models can enhance image quality when conditioned on ground truth labels. Here, we conduct a comprehensive experimental study on image-level conditioning for diffusion models using cluster assignments. We investigate how individual clustering determinants, such as the number of clusters and the clustering method, impact image synthesis across three different datasets. Given the optimal number of clusters with respect to image synthesis, we show that cluster-conditioning can achieve state-of-the-art performance, with an FID of 1.67 for CIFAR10 and 2.17 for CIFAR100, along with a strong increase in training sample efficiency. We further propose a novel empirical method to estimate an upper bound for the optimal number of clusters. Unlike existing approaches, we find no significant association between clustering performance and the corresponding cluster-conditional FID scores. The code is available at https://github.com/HHU-MMBS/cedm-official-wavc2025.
Code (1)
Tasks
ClusteringConditional Image GenerationImage ClusteringImage GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. Considering node classification task, based on the i.i.d assumption among node labels, the traditional supervised learn…
Adversarial AttackNode ClassificationLabel-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis
Ultrasound echocardiography is essential for the non-invasive, real-time assessment of cardiac function, but the scarcity of labelled data, driven by privacy restrictions and the complexity of expert annotation, remains …
Video GenerationLocal Graph Clustering with Noisy Labels
The growing interest in machine learning problems over graphs with additional node information such as texts, images, or labels has popularized methods that require the costly operation of processing the entire graph. Ye…
ClusteringGraph ClusteringWhy Are Conditional Generative Models Better Than Unconditional Ones?
Extensive empirical evidence demonstrates that conditional generative models are easier to train and perform better than unconditional ones by exploiting the labels of data. So do score-based diffusion models. In this pa…
Goal-Conditioned Imitation Learning using Score-based Diffusion Policies
We propose a new policy representation based on score-based diffusion models (SDMs). We apply our new policy representation in the domain of Goal-Conditioned Imitation Learning (GCIL) to learn general-purpose goal-specif…
DenoisingImitation LearningRobot Manipulation