arXiv:2409.19727v3 Announce Type: replace Abstract: Deep Neural Networks (DNNs) are often over-parameterized for their tasks and can be compressed quite drastically by removing weights, a process called pruning. We investigate the impact of different pruning techniques on the classification performance and interpretability of GoogLeNet. We systematically apply unstructured and structured pruning, as well as connection sparsity (pruning of input weights) methods to the network and analyze the outcomes regarding the network's performance on the validation set of ImageNet. We also compare differe
The proliferation of complex deep neural networks has necessitated advances in efficiency and resource management, making pruning techniques a critical area of research right now.
Improving the efficiency and interpretability of AI models through pruning can significantly reduce computational costs and enhance trustworthiness in AI applications.
This research provides deeper insights into the trade-offs between model compression, performance, and interpretability, potentially guiding the design of more efficient and transparent AI systems.
- · AI developers
- · Cloud computing providers (reduced inference costs)
- · Industries deploying AI at scale
More efficient and compact AI models become available for deployment.
Reduced infrastructure demands for running sophisticated AI, potentially lowering barriers to entry for some applications.
Increased interpretability could foster greater public and regulatory trust in AI systems.
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