Papers Dataset Condensation
“Dataset Condensation” 태그가 달린 논문 56편 · 필터 해제
Diffusion Dataset Condensation: Training Your Diffusion Model Faster with Less Data
Diffusion models have achieved remarkable success in various generative tasks, but training them remains highly resource-intensive, often requiring millions of images and many days of GPU computation. From a data-centric…
Dataset CondensationGPUVideo Dataset Condensation with Diffusion Models
In recent years, the rapid expansion of dataset sizes and the increasing complexity of deep learning models have significantly escalated the demand for computational resources, both for data storage and model training. D…
Computational EfficiencyDataset CondensationDataset DistillationSCORE: Soft Label Compression-Centric Dataset Condensation via Coding Rate Optimization
Dataset Condensation (DC) aims to obtain a condensed dataset that allows models trained on the condensed dataset to achieve performance comparable to those trained on the full dataset. Recent DC approaches increasingly f…
Dataset CondensationDomain GeneralizationInformativenessEnhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation
Dataset condensation aims to synthesize datasets with a few representative samples that can effectively represent the original datasets. This enables efficient training and produces models with performance close to those…
Bilevel OptimizationDataset CondensationA Large-Scale Study on Video Action Dataset Condensation
Dataset condensation has made significant progress in the image domain. Unlike images, videos possess an additional temporal dimension, which harbors considerable redundant information, making condensation even more cruc…
Action RecognitionDataset CondensationDataset DistillationDiversityBi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
Dataset condensation has significantly improved model training efficiency, but its application on devices with different computing power brings new requirements for different data sizes. Thus, condensing multiple scale g…
Dataset CondensationDecomposed Distribution Matching in Dataset Condensation
Dataset Condensation (DC) aims to reduce deep neural networks training efforts by synthesizing a small dataset such that it will be as effective as the original large dataset. Conventionally, DC relies on a costly bi-lev…
Continual LearningDataset CondensationDiversityDiffusion-Augmented Coreset Expansion for Scalable Dataset Distillation
With the rapid scaling of neural networks, data storage and communication demands have intensified. Dataset distillation has emerged as a promising solution, condensing information from extensive datasets into a compact …
Bilevel OptimizationComputational EfficiencyDataset CondensationDataset Distillation+1Towards Model-Agnostic Dataset Condensation by Heterogeneous Models
Abstract. The advancement of deep learning has coincided with the proliferation of both models and available data. The surge in dataset sizes and the subsequent surge in computational requirements have led to the develop…
Dataset CondensationDataset Condensation with Latent Quantile Matching
Dataset condensation (DC) methods aim to learn a smaller synthesized dataset with informative data records to accelerate the training of machine learning models. Current distribution matching (DM) based DC methods learn …
Dataset CondensationGraph LearningMitigating Bias in Dataset Distillation
Dataset Distillation has emerged as a technique for compressing large datasets into smaller synthetic counterparts, facilitating downstream training tasks. In this paper, we study the impact of bias inside the original d…
Dataset CondensationDataset DistillationDensity EstimationCondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
Dataset condensation is a newborn technique that generates a small dataset that can be used in training deep neural networks to lower training costs. The objective of dataset condensation is to ensure that the model trai…
Dataset CondensationTime SeriesTime Series ForecastingDANCE: Dual-View Distribution Alignment for Dataset Condensation
Dataset condensation addresses the problem of data burden by learning a small synthetic training set that preserves essential knowledge from the larger real training set. To date, the state-of-the-art (SOTA) results are …
Dataset CondensationSpectral Greedy Coresets for Graph Neural Networks
The ubiquity of large-scale graphs in node-classification tasks significantly hinders the real-world applications of Graph Neural Networks (GNNs). Node sampling, graph coarsening, and dataset condensation are effective s…
Dataset CondensationNode ClassificationCalibrated Dataset Condensation for Faster Hyperparameter Search
Dataset condensation can be used to reduce the computational cost of training multiple models on a large dataset by condensing the training dataset into a small synthetic set. State-of-the-art approaches rely on matching…
Dataset CondensationKoopcon: A new approach towards smarter and less complex learning
In the era of big data, the sheer volume and complexity of datasets pose significant challenges in machine learning, particularly in image processing tasks. This paper introduces an innovative Autoencoder-based Dataset C…
Dataset CondensationElucidating the Design Space of Dataset Condensation
Dataset condensation, a concept within data-centric learning, efficiently transfers critical attributes from an original dataset to a synthetic version, maintaining both diversity and realism. This approach significantly…
Dataset CondensationDiversityDistilling Datasets Into Less Than One Image
Dataset distillation aims to compress a dataset into a much smaller one so that a model trained on the distilled dataset achieves high accuracy. Current methods frame this as maximizing the distilled classification accur…
Dataset CondensationDataset DistillationDataset Distillation - 1IPCDataset Condensation for Time Series Classification via Dual Domain Matching
Time series data has been demonstrated to be crucial in various research fields. The management of large quantities of time series data presents challenges in terms of deep learning tasks, particularly for training a dee…
Data AugmentationDataset CondensationTime SeriesTime Series ClassificationMultisize Dataset Condensation
While dataset condensation effectively enhances training efficiency, its application in on-device scenarios brings unique challenges. 1) Due to the fluctuating computational resources of these devices, there's a demand f…
Dataset Condensation