This is a pytorch implementation of the Muti-task Learning using CNN + AutoEncoder. Cifar10 is available for the datas et by default. You can also use your own dataset. epoch,train loss,train accuracy ...

ABSTRACT: With the deepening of oil and gas exploration, the exploration targets have gradually shifted from structural oil and gas reservoirs to lithological oil and gas reservoirs. The fluvial ...

Google's TorchTPU aims to enhance TPU compatibility with PyTorch Google seeks to help AI developers reduce reliance on Nvidia's CUDA ecosystem TorchTPU initiative is part of Google's plan to attract ...

Point of care ultrasound (POCUS) is commonly used for diagnostic triage of internal injuries in both civilian and military trauma. In resource constrained environments, such as mass-casualty ...

SVG Autoencoder - Uses a frozen representation encoder with a residual branch to compensate the information loss and a learned convolutional decoder to transfer the SVG latent space to pixel space.

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...

Abstract: This work addresses the challenge of the portability of Autoencoder models for the lossy compression of different spatially independent and unknown hyperspectral satellite data. We propose ...