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TCuPGAN: A novel framework developed for optimizing human-machine interactions in citizen science
Using this model alongside citizen science projects which use 3D datasets (image cubes) on the Zooniverse platforms, we propose an iterative human-machine optimization framework where only a fraction of the 2D slices from these cubes are seen by the volunteers.
From fat droplets to floating forests: cross-domain transfer learning using a PatchGAN-based segmentation model
We compare and contrast its performance with a TL model based on the COCO image set, and subsequently with baseline counterparts.
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