DPaI: Differentiable Pruning at Initialization with Node-Path Balance Principle
Keywords: Prunning at Initialization, Sparsity, Neural Architecture Search
TL;DR: Our paper proposed a differentiable pruning at initialization methods that achived significantly better performance on pruning at initialization tasks.
Abstract: Pruning at Initialization (PaI) is a technique in neural network optimization characterized by the proactive elimination of weights before the network's training on designated tasks. This innovative strategy potentially reduces the costs for training and inference, significantly advancing computational efficiency. A key element of PaI's effectiveness is that it considers the significance of weights in an untrained network. It prioritizes the trainability and optimization potential of the pruned subnetworks. Recent methods can effectively prevent the formation of hard-to-optimize networks, e.g. through iterative adjustments at each network layer. However, this way often results in *large-scale discrete optimization problems*, which could make PaI further challenging. This paper introduces a novel method, called *DPaI*, that involves a differentiable optimization of the pruning mask. DPaI adopts a dynamic and adaptable pruning process, allowing easier optimisation processes and better solutions. More importantly, our differentiable formulation enables readily use of the existing rich body of efficient gradient-based methods for PaI. Our empirical results demonstrate that DPaI significantly outperforms current state-of-the-art PaI methods on various architectures, such as Convolutional Neural Networks and Vision-Transformers.
Primary Area: other topics in machine learning (i.e., none of the above)
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Submission Number: 4990
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