New AI research proposes a novel approach for compact and optimal deep learning by decoupling model DoF and model parameters

In deep learning, large models with millions of parameters have shown remarkable accuracy in various applications such as image recognition, natural language processing, and speech recognition. However, training and implementing these models can be computationally expensive and require significant memory resources. This has led to a growing need for more efficient deep learning models that can be trained and deployed to resource-constrained devices such as smartphones, embedded systems, and Internet of Things (IoT) devices. In addition, reducing computational and memory requirements can also help reduce the environmental impact of deep learning by reducing power consumption and carbon footprint. Therefore, there is a need for new techniques and approaches to reduce the computational and memory requirements of deep learning models while maintaining or even improving accuracy.

Various attempts have been made to reduce the computational and memory requirements of large models while maintaining accuracy. A common approach is to use model compression techniques, such as pruning or quantization, to reduce the number of parameters in a model. Another method is to use low rank approximations to reduce the memory footprint of a model. However, these approaches often require extensive training and optimization procedures, and the resulting models can still be computationally expensive.

Recently, a US research team proposed a new method that takes a different approach by decoupling degrees of freedom (DoF) and the actual number of parameters in a model. This allows for a more flexible optimization process and can potentially result in accurate and computationally efficient models.

To achieve this, the researchers create a recurring parameter generator (RPG) that repeatedly retrieves the parameters of a ring and unpacks them into a large model with random permutation and sign change to promote parameter decorrelation. The RPG operates on a one-stage end-to-end learning process, allowing gradient descent to find the best model under constraints with faster convergence.

The researchers found a log-linear relationship between model DoF and accuracy, which means that reducing the number of DoFs required for a deep learning model does not necessarily result in a loss of accuracy. Instead, at a large enough DoF, the RPG removes redundancy and often finds a model with little loss of accuracy.

In addition, RPG achieves the same accuracy of ImageNet with half the DoF of ResNet-vanilla and outperforms other state-of-the-art compression approaches. The RPG can be further trimmed and quantized for additional runtime performance gain.

Overall, the proposed method presents significant potential for the efficient and practical implementation of deep learning models by reducing the number of DoFs required without sacrificing accuracy.

To assess how well the suggested strategy works, a series of experiments were performed to measure its effectiveness in improving overall system performance. The results show that ResNet-RPG optimizes on a parameter subspace with fewer degrees of freedom than the standard model, leading to a faster rate of convergence. ResNet-RPG outperforms state-of-the-art compression methods on ImageNet while achieving lower gaps between training and validation sets, indicating less overfitting. In addition, ResNet-RPG has higher out-of-distribution performance even with smaller model degrees of freedom. ResNet-RPG model file storage space is significantly reduced, with a save file size of just 23MB (49% reduction) with no loss of accuracy and 9.5MB (79% reduction) with only a loss precision of two percentage points. Additionally, ResNet-RPG models can be quantized to further reduce size without a significant drop in accuracy. The proposed method also provides a security advantage by using permutation matrices generated by the random seed as security keys.

In summary, the proposed approach of decoupling the degrees of freedom and the actual number of parameters in a model through a recursive parameter generator (RPG) presents significant potential for the efficient and practical deployment of deep learning models. Experiments show that RPG outperforms state-of-the-art compression methods, achieving lower gaps between training and validation sets, less overfitting, higher out-of-distribution performance, and significantly reduced model file size. In general, the RPG provides a more flexible optimization process and a faster rate of convergence, allowing for computationally efficient and accurate models that can be trained and deployed to resource-constrained devices.

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