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Dataset Distillation by Automatic Training Trajectories (2407.14245v1)

Published 19 Jul 2024 in cs.CV

Abstract: 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 prioritize long-range matching, involving the unrolling of training trajectories with a fixed number of steps (NS) on the synthetic dataset to align with various expert training trajectories. However, traditional long-range matching methods possess an overfitting-like problem, the fixed step size NS forces synthetic dataset to distortedly conform seen expert training trajectories, resulting in a loss of generality-especially to those from unencountered architecture. We refer to this as the Accumulated Mismatching Problem (AMP), and propose a new approach, Automatic Training Trajectories (ATT), which dynamically and adaptively adjusts trajectory length NS to address the AMP. Our method outperforms existing methods particularly in tests involving cross-architectures. Moreover, owing to its adaptive nature, it exhibits enhanced stability in the face of parameter variations.

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Authors (5)
  1. Dai Liu (6 papers)
  2. Jindong Gu (101 papers)
  3. Hu Cao (17 papers)
  4. Carsten Trinitis (4 papers)
  5. Martin Schulz (30 papers)
Citations (5)

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