Dataset distillation is the task of synthesizing a small dataset such that a model trained on
the synthetic set will match the test accuracy of the model trained on the full dataset. In this …

Dataset distillation aims to synthesize small datasets with little information loss from original
large-scale ones for reducing storage and training costs. Recent state-of-the-art methods …

Model distillation aims to distill the knowledge of a complex model into a simpler one. In this
paper, we consider an alternative formulation called dataset distillation: we keep the model …

Recommender system is an essential component of web services to engage users. Popular
recommender systems model user preferences and item properties using a large amount of …

Self-paced learning (SPL) is a recently proposed learning regime inspired by the learning
process of humans and animals that gradually incorporates easy to more complex samples …

We study the problem of dataset distillation-creating a small set of synthetic examples
capable of training a good model. In particular, we study the problem of label distillation …

Background and objective: Sharing of medical data is required to enable the cross-agency
flow of healthcare information and construct high-accuracy computer-aided diagnosis …

Self-paced learning (SPL) is a recently proposed methodology designed by mimicking
through the learning principle of humans/animals. A variety of SPL realization schemes have …

In a weakly-supervised scenario object detectors need to be trained using image-level
annotation alone. Since bounding-box-level ground truth is not available, most of the …

The problem of learning an image classifier that allows detection of out-of-distribution (OOD)
examples, with the help of auxiliary background datasets, is studied. While training with …