What are the potential societal impacts of widespread adoption of image classification technology?

Mar 19, 2025 ... Image classification is a technique that involves analyzing the content of an image to attach tags or labels to it. This distributes the images into different ... Jan 16, 2025 ... AbstractMotivation. The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. We use an autocatalytic reaction, the copper-catalyzed azide–alkyne cycloaddition, to perform digital image recognition tasks. The idea is very simple: instead of finding the single closest image in the training set, we will find the top k closest images, and have them vote on the label ... Apr 11, 2025 ... This preview model is a retrainable deep learning model used to classify images. You can train it on your own data and create an ML Skill to ... Jun 5, 2023 ... Self-Supervised Learning (SSL) is a new training paradigm that can be used to train DL models to classify images without the bottleneck of having well-defined ... Apr 4, 2025 ... Hybrid text image classifier. HTIC models—which mix an optimal CNN and VGG16 for image data with ROBERTA and MySQL for text data—achieve ... Jul 10, 2023 ... Remote cameras have become a widespread data-collection tool for terrestrial mammals, but classifying images can be labor intensive and ... Image classification is the process of categorizing entire images into different groups (classes) based on their content. Jul 22, 2024 ... Data imbalance and bias are significant hurdles in image classification. Poor or biased training data can severely impact model performance.