Existing dataset distillation (DD) techniques typically rely on iterative strategies to synthesize
condensed datasets, where datasets before and after distillation are forward and backward …

Dataset Distillation is used to create a concise, yet informative, synthetic dataset that can
replace the original dataset for training purposes. Some leading methods in this domain …

Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in
substantial training and storage costs. To address these challenges, dataset condensation …

Dataset Condensation has emerged as a technique for compressing large datasets into
smaller synthetic counterparts, facilitating downstream training tasks. In this paper, we study …

Training deep neural networks (DNNs) on largescale datasets is often inefficient with large
computational needs and significant energy consumption. Although great efforts have been …

Large machine-learning training datasets can be distilled into small collections of
informative synthetic data samples. These synthetic sets support efficient model learning …

In the era of deep learning, training deep neural networks often requires extensive data,
leading to substantial costs. Dataset condensation addresses this by learning a small …

While deep learning techniques have proven successful in image-related tasks, the
exponentially increased data storage and computation costs become a significant …

In an age marked by an unprecedented influx of data across diverse domains, the quest for
effective machine learning (ML) solutions has increased significantly. However, data …

In the realm of Internet of Things (IoT) systems, the generation of cryptographic keys is
crucial for ensuring secure data transmission and device authentication. However …