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Found 4 papers, 2 papers with code
Discrete Latent Graph Generative Modeling with Diffusion Bridges
Learning graph generative models over latent spaces has received less attention compared to models that operate on the original data space and has so far demonstrated lacklustre performance.
Improving VAE generations of multimodal data through data-dependent conditional priors
We propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), which learns to differentiate between the individual mixture components and therefore allows for generations from the distributional data clusters.
Variational Saccading: Efficient Inference for Large Resolution Images
Image classification with deep neural networks is typically restricted to images of small dimensionality such as 224 x 244 in Resnet models [24].
Continual Classification Learning Using Generative Models
Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences.
