What are some frameworks for distributed deep learning?

Jul 22, 2024 ... Data imbalance and bias are significant hurdles in image classification. Poor or biased training data can severely impact model performance. This article describes a solution idea. Your cloud architect can use this guidance to help visualize the major components for a typical implementation of this ... GhostImage: Remote Perception Attacks against Camera-based Image Classification Systems. Authors: Yanmao Man and Ming Li, University of Arizona; Ryan Gerdes ... Oct 13, 2021 ... We study the effectiveness of self-supervised contrastive learning as a pre-training strategy within the domain of medical image classification. Jan 16, 2025 ... AbstractMotivation. The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. ... image logging platform that combines image classification and calorie estimation to support dietary management. This thesis consists of four studies ... Nov 23, 2022 ... Image classification is a computer vision task where label(s) are assigned to an entire image. The label should be representative of the main ... Image classification is the process of assigning land cover classes to pixels. For example, classes include water, urban, forest, agriculture, and grassland. Image classification refers to the task of assigning classes—defined in a land cover and land use classification system, known as the schema—to all the pixels ... Image Classification can be broadly divided into either Binary or Multi-class problems depending on the number of categories. Binary image classification ...