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Articles3
StreamingVLM: Real-Time Vision-Language Model for Infinite Video Stream Processing
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Efficient Training Data Reduction Using High-Fidelity Labels and Human Expertise
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Analyzing Loss Functions in Diffusion Bridge Samplers
Diffusion bridges in deep-learning methods for sampling from unnormalized distributions are analyzed, comparing the performance of Log Variance (LV) loss and reverse Kullback-Leibler (rKL) loss. The study shows that rKL loss with the log-derivative trick consistently outperforms LV loss, especially for diffusion bridges with learned diffusion coefficients.
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