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A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification
Our goal is to optimize DL models such that the encoder latent requires low channel bandwidth while still delivers feature information for high classification accuracy.
UFed-GAN: A Secure Federated Learning Framework with Constrained Computation and Unlabeled Data
To satisfy the broad applications and insatiable hunger for deploying low latency multimedia data classification and data privacy in a cloud-based setting, federated learning (FL) has emerged as an important learning paradigm.
End-to-End Optimization of JPEG-Based Deep Learning Process for Image Classification
Among major deep learning (DL) applications, distributed learning involving image classification require effective image compression codecs deployed on low-cost sensing devices for efficient transmission and storage.
Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Images
The MLA-GDCC includes a white balance (WB) to correct the color imbalance on the images, a modified U-Net and a support vector machine (SVM) to segment the graphene flakes.
