Abstract: The growth of interconnected devices has led to an enormous volume of temporal data that requires specialized compression models for efficient storage. Besides this, most applications need ...
To use our MODVAE models, please download/clone the whole repository from Github to your local. For the best-performing model, cXVAE, we've created a wrapper file ...
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: The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, ...
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