Abstract: This paper introduces V2Coder, a non-autoregressive vocoder based on hierarchical variational autoencoders (VAEs). The hierarchical VAE with hierarchically extended prior and approximate ...
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 ...
MAESTRO: Masked Autoencoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data
MAESTRO_FLAIR-HUB_base — pre-trained on FLAIR-HUB MAESTRO_S2-NAIP-urban_base — pre-trained on S2-NAIP-urban Land cover segmentation in France, with 12 semantic classes. Note that the FLAIR#2 version ...
Abstract: Variational Graph Autoencoders (VAGE) emerged as powerful graph representation learning methods with promising performance on graph analysis tasks. However, existing methods typically rely ...
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