Papers Dataset Distillation - 1IPC
“Dataset Distillation - 1IPC” 태그가 달린 논문 11편 · 필터 해제
Distilling 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 - 1IPCEmbarassingly Simple Dataset Distillation
Dataset distillation extracts a small set of synthetic training samples from a large dataset with the goal of achieving competitive performance on test data when trained on this sample. In this work, we tackle dataset di…
Bilevel OptimizationDataset DistillationDataset Distillation - 1IPCDataset Distillation with Convexified Implicit Gradients
We propose a new dataset distillation algorithm using reparameterization and convexification of implicit gradients (RCIG), that substantially improves the state-of-the-art. To this end, we first formulate dataset distill…
Dataset DistillationDataset Distillation - 1IPCMinimizing the Accumulated Trajectory Error to Improve Dataset Distillation
Model-based deep learning has achieved astounding successes due in part to the availability of large-scale real-world data. However, processing such massive amounts of data comes at a considerable cost in terms of comput…
Dataset DistillationDataset Distillation - 1IPCNeural Architecture SearchScaling Up Dataset Distillation to ImageNet-1K with Constant Memory
Dataset Distillation is a newly emerging area that aims to distill large datasets into much smaller and highly informative synthetic ones to accelerate training and reduce storage. Among various dataset distillation meth…
Dataset DistillationDataset Distillation - 1IPCGPURemember the Past: Distilling Datasets into Addressable Memories for Neural Networks
We propose an algorithm that compresses the critical information of a large dataset into compact addressable memories. These memories can then be recalled to quickly re-train a neural network and recover the performance …
Continual LearningDataset DistillationDataset Distillation - 1IPCDataset Distillation using Neural Feature Regression
Dataset distillation aims to learn a small synthetic dataset that preserves most of the information from the original dataset. Dataset distillation can be formulated as a bi-level meta-learning problem where the outer lo…
Continual LearningDataset DistillationDataset Distillation - 1IPCImage Classification+2Dataset Distillation by Matching Training Trajectories
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 paper, we propose a new formu…
Dataset DistillationDataset Distillation - 1IPCDataset Condensation with Distribution Matching
Computational cost of training state-of-the-art deep models in many learning problems is rapidly increasing due to more sophisticated models and larger datasets. A recent promising direction for reducing training cost is…
Continual LearningDataset CondensationDataset Distillation - 1IPCNeural Architecture SearchDataset Condensation with Differentiable Siamese Augmentation
In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load. In this paper, we focus on condensing large traini…
Continual LearningData AugmentationDataset CondensationDataset Distillation - 1IPC+3Dataset Condensation with Gradient Matching
As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive. This paper proposes a training set synthesis tech…
Continual LearningDataset CondensationDataset Distillation - 1IPCDomain Adaptation+1