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On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm
Contemporary machine learning which involves training large neural networks on massive
datasets faces significant computational challenges. Dataset distillation as a recent …
datasets faces significant computational challenges. Dataset distillation as a recent …
Does graph distillation see like vision dataset counterpart?
Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …
learning, but its cost and storage have attracted increasing concerns. Existing graph …
Dataset regeneration for sequential recommendation
The sequential recommender (SR) system is a crucial component of modern recommender
systems, as it aims to capture the evolving preferences of users. Significant efforts have …
systems, as it aims to capture the evolving preferences of users. Significant efforts have …
Dataset distillation by automatic training trajectories
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 …
replace the original dataset for training purposes. Some leading methods in this domain …
Navigating complexity: Toward lossless graph condensation via expanding window matching
Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing
a compact counterpart without sacrificing the performance of Graph Neural Networks …
a compact counterpart without sacrificing the performance of Graph Neural Networks …
ATOM: attention mixer for efficient dataset distillation
Recent works in dataset distillation seek to minimize training expenses by generating a
condensed synthetic dataset that encapsulates the information present in a larger real …
condensed synthetic dataset that encapsulates the information present in a larger real …
Exploring the impact of dataset bias on dataset distillation
Dataset Distillation (DD) is a promising technique to synthesize a smaller dataset that
preserves essential information from the original dataset. This synthetic dataset can serve as …
preserves essential information from the original dataset. This synthetic dataset can serve as …
Generative dataset distillation: Balancing global structure and local details
In this paper we propose a new dataset distillation method that considers balancing global
structure and local details when distilling the information from a large dataset into a …
structure and local details when distilling the information from a large dataset into a …
Data distillation can be like vodka: Distilling more times for better quality
Dataset distillation aims to minimize the time and memory needed for training deep networks
on large datasets, by creating a small set of synthetic images that has a similar …
on large datasets, by creating a small set of synthetic images that has a similar …
Color-oriented redundancy reduction in dataset distillation
Dataset Distillation (DD) is designed to generate condensed representations of extensive
image datasets, enhancing training efficiency. Despite recent advances, there remains …
image datasets, enhancing training efficiency. Despite recent advances, there remains …