Page 2 - Contrastive Learning News Today

Recreating the used bookstore experience via contrastive learning, t-SNE, and 100M book reviews.

The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning. Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive and ...

Qosmo offers a license to the updated multi-modal AI technology for commercial use

DALL-E 2, OpenAI's new text-to-image generator, is better than its predecessor, but comes with a debatable business model.

MIT researchers have developed a technique to train a machine-learning model for image classification, which does not require the use of a dataset. Instead, they use a “generative model” to produce synthetic data that is used to train an image classifier, which can then perform as well as or better than an image classifier trained using real data.

Technologies of object segmentation and tracking for autonomous driving jointly developed by the Korean research team and the research team of the University of Washington won the first place prize in an International competition of tracking objects in the field of autonomous driving.

With the prevalence of smart devices, billions of people are accessing digital resource in their daily life. Online user-behavior modeling, as such, has been actively researched in recent years. However, due to the data uncertainty (sparse-ness and skewness), traditional techniques suffer from certain drawbacks, such as relying on labor-intensive expertise or prior knowledge, lacking of interpretability and transparency, and expensive computational cost. As a step toward bridging the gap, this p...