Abstract: Vector quantized variational autoencoders, as variants of variational autoencoders, effectively capture discrete representations by quantizing continuous latent spaces and are widely used in ...
For the first time, a research team has demonstrated an artificial intelligence semiconductor technology that integrates the ...
Abstract: In this paper, we introduce a novel inversion methodology employing the variational autoencoder (VAE) for human thorax attenuation tomography using low-frequency ultrasound. The VAE is ...
We present Representation Autoencoders (RAE), a class of autoencoders that utilize pretrained, frozen representation encoders such as DINOv2 and SigLIP2 as encoders with trained ViT decoders. RAE can ...
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