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Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation (2211.11004v3)

Published 20 Nov 2022 in cs.LG, cs.AI, and cs.CV

Abstract: 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 computations, storage, training and the search for good neural architectures. Dataset distillation has thus recently come to the fore. This paradigm involves distilling information from large real-world datasets into tiny and compact synthetic datasets such that processing the latter ideally yields similar performances as the former. State-of-the-art methods primarily rely on learning the synthetic dataset by matching the gradients obtained during training between the real and synthetic data. However, these gradient-matching methods suffer from the so-called accumulated trajectory error caused by the discrepancy between the distillation and subsequent evaluation. To mitigate the adverse impact of this accumulated trajectory error, we propose a novel approach that encourages the optimization algorithm to seek a flat trajectory. We show that the weights trained on synthetic data are robust against the accumulated errors perturbations with the regularization towards the flat trajectory. Our method, called Flat Trajectory Distillation (FTD), is shown to boost the performance of gradient-matching methods by up to 4.7% on a subset of images of the ImageNet dataset with higher resolution images. We also validate the effectiveness and generalizability of our method with datasets of different resolutions and demonstrate its applicability to neural architecture search. Code is available at https://github.com/AngusDujw/FTD-distillation.

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Authors (5)
  1. Jiawei Du (31 papers)
  2. Yidi Jiang (18 papers)
  3. Vincent Y. F. Tan (205 papers)
  4. Joey Tianyi Zhou (116 papers)
  5. Haizhou Li (286 papers)
Citations (85)
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Summary

Analysis of Dataset Distillation via Flat Trajectory Approach

This paper by Du et al., "Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation," addresses the challenges inherent in deep learning tasks which require extensive computation resources—specifically, those associated with handling large-scale datasets. The authors propose an innovative method termed "Flat Trajectory Distillation" (FTD) which seeks to mitigate the accumulated trajectory error prevalent in existing dataset distillation techniques.

In dataset distillation, the goal is to condense a large real-world dataset into a much smaller synthetic dataset that can still train models to perform comparably to those trained on the original data. Traditional approaches, particularly gradient-matching methods, often face discrepancies between the training and evaluation phases. The accumulated trajectory error arises as a result, degrading performance.

Methodology Overview

The paper primarily critiques gradient-matching methods such as Matching Training Trajectories (MTT) and proposes improvements via FTD. The key innovation of FTD lies in encouraging a flat trajectory during training, which enhances robustness against perturbations of weights. By regularizing the teacher trajectories to be flat, the synthetic dataset is distilled to ensure it can effectively generalize without accumulating errors during model evaluation. This approach contrasts with robust learning techniques, which introduce artificial noise but can inadvertently degrade performance when the amount of distilled information is limited.

Numerical Results and Comparative Performance

Du et al. employ empirical evidence to substantiate their claims, particularly highlighting the degradation of effectiveness when standard methods encounter trajectory discrepancies. The results indicate that with 10 images per class (ipc), FTD can improve the performance by up to 4.7% compared to existing gradient-matching methods on high-resolution datasets like ImageNet subsets. This substantial improvement underscores FTD's potential to synthesize effective synthetic datasets across various resolutions.

Furthermore, the paper demonstrates cross-architecture generalization capabilities, which are crucial for practical applications where model architectures may differ from those used in the distillation phase. Experiments on CIFAR-10 with different network architectures including ResNet, VGG, and AlexNet validate FTD's strong generalization potential, showing consistent improvements over previous methods.

Implications and Future Directions

The implications of the proposed method are profound, offering a significant reduction in computational resources without sacrificing model performance. FTD not only provides a strategic advancement for tasks like neural architecture search (NAS) but also sets a precedent for future research to explore more robust distillation methods.

The theoretical discussions surrounding flat minima and their relation to generalization further underscore the importance of minimizing trajectory error. The paper invites a deeper exploration into the initialization effects and landscape geometry inherent to distillation processes.

Future research could delve into optimizing the flat trajectory by improving sharpness-aware minimization techniques or explore applications of dataset distillation beyond NAS. As models continue to scale, finding more efficient ways to distill datasets in a manner that preserves their utility and informational content remains a critical area of paper.

In conclusion, the paper contributes valuable insights to the ongoing efforts to optimize deep learning training processes, effectively balancing resource demands with model accuracy and generalization. This work reflects a thoughtful pursuit of methodological refinement essential in the evolving landscape of artificial intelligence.