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Refining generative process with discriminator guidance in score-based diffusion models
The proposed method, Discriminator Guidance, aims to improve sample generation of pre-
trained diffusion models. The approach introduces a discriminator that gives explicit …
trained diffusion models. The approach introduces a discriminator that gives explicit …
Score-based generative diffusion models for social recommendations
With the prevalence of social networks on online platforms, social recommendation has
become a vital technique for enhancing personalized recommendations. The effectiveness …
become a vital technique for enhancing personalized recommendations. The effectiveness …
Closed-Loop Unsupervised Representation Disentanglement with -VAE Distillation and Diffusion Probabilistic Feedback
Abstract Representation disentanglement may help AI fundamentally understand the real
world and thus benefit both discrimination and generation tasks. It currently has at least …
world and thus benefit both discrimination and generation tasks. It currently has at least …
Label-noise robust diffusion models
Conditional diffusion models have shown remarkable performance in various generative
tasks, but training them requires large-scale datasets that often contain noise in conditional …
tasks, but training them requires large-scale datasets that often contain noise in conditional …
Diffusion Bridge AutoEncoders for Unsupervised Representation Learning
Diffusion-based representation learning has achieved substantial attention due to its
promising capabilities in latent representation and sample generation. Recent studies have …
promising capabilities in latent representation and sample generation. Recent studies have …