blog
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Training Energy-Based Normalizing Flow with Score-Matching Objectives
This blog post offers an introduction to our proposed EBFlow modeling method. First, we begin with an overview of flow-based and energy-based models. Then, we explore the connections between these models by introducing EBFlow. Next, we present experimental results to demonstrate the effectiveness of the proposed method. Finally, we discuss several implications of the EBFlow formula.
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On Investigating the Conservative Property of Score-Based Generative Models
This blog post provides an introduction to our proposed QCSBM modeling method. We first start with motivational examples that examine the influence of the conservative property of score-based models. Next, we outline a training pipeline designed to backpropagate the conservativeness through the model. Finally, we present experimental results to demonstrate the effectiveness of our method.
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Denoising Likelihood Score Matching for Conditional Score-based Data Generation
This blog post provides an introduction to our proposed DLSM training method. First, we start with an introduction of the denoising score-matching (DSM) method. Then, we discuss the limitations of the current conditional score-based generation methods. Next, we formulate the proposed DLSM loss. Finally, we present experimental results to demonstrate the effectiveness of DLSM.
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Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation
End-to-end ensemble learning methods often lack flexibility as any modification to the ensemble requires retraining of their frameworks. To address this problem, we propose a flexible ensemble-distillation framework for performing semantic segmentation based UDA, allowing any arbitrary composition of the members in the ensemble while still maintaining its superior performance. To achieve such flexibility, our framework is designed to be robust against the output inconsistency and the performance variation of the members within the ensemble.