Calibrated Dataset Condensation for Faster Hyperparameter Search

• URL: http://arxiv.org/abs/2405.17535v1
• Date: Mon, 27 May 2024 17:55:01 GMT
• Title: Calibrated Dataset Condensation for Faster Hyperparameter Search
• Authors: Mucong Ding, Yuancheng Xu, Tahseen Rabbani, Xiaoyu Liu, Brian Gravelle, Teresa Ranadive, Tai-Ching Tuan, Furong Huang,
• Abstract summary: State-of-the-art approaches rely on matching the model gradients between the real and synthetic data. This paper considers a different condensation objective specifically geared toward hyperparameter search.
• Score: 23.790315967011345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 the model gradients between the real and synthetic data. However, there is no theoretical guarantee of the generalizability of the condensed data: data condensation often generalizes poorly across hyperparameters/architectures in practice. This paper considers a different condensation objective specifically geared toward hyperparameter search. We aim to generate a synthetic validation dataset so that the validation-performance rankings of the models, with different hyperparameters, on the condensed and original datasets are comparable. We propose a novel hyperparameter-calibrated dataset condensation (HCDC) algorithm, which obtains the synthetic validation dataset by matching the hyperparameter gradients computed via implicit differentiation and efficient inverse Hessian approximation. Experiments demonstrate that the proposed framework effectively maintains the validation-performance rankings of models and speeds up hyperparameter/architecture search for tasks on both images and graphs.

Related papers

• Hierarchical Features Matter: A Deep Exploration of GAN Priors for Improved Dataset Distillation [51.44054828384487]
  We propose a novel parameterization method dubbed Hierarchical Generative Latent Distillation (H-GLaD) This method systematically explores hierarchical layers within the generative adversarial networks (GANs) In addition, we introduce a novel class-relevant feature distance metric to alleviate the computational burden associated with synthetic dataset evaluation.
  arXiv  Detail & Related papers  (2024-06-09T09:15:54Z)
• CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting [22.473436770730657]
  The objective of dataset condensation is to ensure that the model trained with the synthetic dataset can perform comparably to the model trained with full datasets. In classification, the synthetic data is considered well-distilled if the model trained with the full dataset and the model trained with the synthetic dataset yield identical labels for the same input. In TS-forecasting, the effectiveness of synthetic data distillation is determined by the distance between predictions of the two models.
  arXiv  Detail & Related papers  (2024-06-04T09:18:20Z)
• Latent Semantic Consensus For Deterministic Geometric Model Fitting [109.44565542031384]
  We propose an effective method called Latent Semantic Consensus (LSC) LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses. LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting.
  arXiv  Detail & Related papers  (2024-03-11T05:35:38Z)
• Improved Distribution Matching for Dataset Condensation [91.55972945798531]
  We propose a novel dataset condensation method based on distribution matching. Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources.
  arXiv  Detail & Related papers  (2023-07-19T04:07:33Z)
• Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching [7.908244841289913]
  The expenses involved in training state-of-the-art deep hashing retrieval models have witnessed an increase. The state-of-the-art dataset distillation methods can not expand to all deep hashing retrieval methods. We propose an efficient condensation framework that addresses these limitations by matching the feature-embedding between synthetic set and real set.
  arXiv  Detail & Related papers  (2023-05-29T13:23:55Z)
• Dataset Condensation via Efficient Synthetic-Data Parameterization [40.56817483607132]
  Machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning. Recent studies on dataset condensation attempt to reduce the dependence on such massive data by synthesizing a compact training dataset. We propose a novel condensation framework that generates multiple synthetic data with a limited storage budget via efficient parameterization considering data regularity.
  arXiv  Detail & Related papers  (2022-05-30T09:55:31Z)
• AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning [72.54359545547904]
  We propose a gradient-based subset selection framework for hyper- parameter tuning. We show that using gradient-based data subsets for hyper- parameter tuning achieves significantly faster turnaround times and speedups of 3$times$-30$times$.
  arXiv  Detail & Related papers  (2022-03-15T19:25:01Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.