Accelerating Learned Image Compression Through Modeling Neural Training Dynamics

• URL: http://arxiv.org/abs/2505.18107v1
• Date: Fri, 23 May 2025 17:03:13 GMT
• Title: Accelerating Learned Image Compression Through Modeling Neural Training Dynamics
• Authors: Yichi Zhang, Zhihao Duan, Yuning Huang, Fengqing Zhu,
• Abstract summary: This paper takes a step forward in accelerating the training of LIC methods by modeling the neural training dynamics.<n>We first propose a Sensitivity-aware True and Dummy Embedding Training mechanism (STDET) that clusters LIC model parameters into few separate modes.<n>By further utilizing the stable intra-mode correlations throughout training and parameter sensitivities, we gradually embed non-reference parameters, reducing the number of trainable parameters.
• Score: 11.729071258457138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As learned image compression (LIC) methods become increasingly computationally demanding, enhancing their training efficiency is crucial. This paper takes a step forward in accelerating the training of LIC methods by modeling the neural training dynamics. We first propose a Sensitivity-aware True and Dummy Embedding Training mechanism (STDET) that clusters LIC model parameters into few separate modes where parameters are expressed as affine transformations of reference parameters within the same mode. By further utilizing the stable intra-mode correlations throughout training and parameter sensitivities, we gradually embed non-reference parameters, reducing the number of trainable parameters. Additionally, we incorporate a Sampling-then-Moving Average (SMA) technique, interpolating sampled weights from stochastic gradient descent (SGD) training to obtain the moving average weights, ensuring smooth temporal behavior and minimizing training state variances. Overall, our method significantly reduces training space dimensions and the number of trainable parameters without sacrificing model performance, thus accelerating model convergence. We also provide a theoretical analysis on the Noisy quadratic model, showing that the proposed method achieves a lower training variance than standard SGD. Our approach offers valuable insights for further developing efficient training methods for LICs.

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