Abstract: Vibration acceleration signals allow humans to perceive the surface characteristics of textures during tool-surface interactions. However, acquiring acceleration signals requires a ...
Abstract: Variational autoencoders (VAEs) are challenged by the imbalance between representation inference and task fitting caused by surrogate loss. To address this issue, existing methods adjust ...
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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