Search Results for "image processing framework"

CycleGAN — in its original form — is a landmark in deep learning for image-to-image translation without paired data. Rather than requiring matching image pairs between source and target domains (which are often hard or impossible to obtain), CycleGAN learns two mappings — one from domain A to B, and another back from B to A — along with a cycle-consistency loss that encourages the round-trip to reconstruct the original image. This innovation lets the model learn domain-to-domain translations...

MultiPathNet is a Torch-7 implementation of the “A MultiPath Network for Object Detection” paper (BMVC 2016), developed by Facebook AI Research. It extends the Fast R-CNN framework by introducing multiple network “paths” to enhance feature extraction and object recognition robustness. The MultiPath architecture incorporates skip connections and multi-scale processing to capture both fine-grained details and high-level context within a single detection pipeline. This results in improved detection accuracy across various object sizes and categories compared to standard single-path architectures. ...