An accuracy-aware extension to LRP-based pruning for CNNs to prevent cascading accuracy degradation in data-scarce transfer learning
arXiv:2511.10861v3 Announce Type: replace-cross Abstract: Convolutional Neural Networks (CNNs) pre-trained on large-scale datasets such as ImageNet are widely used as feature extractors to construct high-accuracy classification models from scarce data for specific tasks. In such scenarios, fine-tuning the pre-trained CNN is difficult due to data scarcity, necessitating the use of fixed weights. However, when the weights are kept fixed, many filters that do not contribute to the target task remain in the model, leading to unnecessary redundancy and reduced efficiency. Therefore, effective metho
The proliferation of specialized AI tasks, especially with limited data, is pushing the need for more efficient and robust models for practical deployment.
This development allows for more accurate and resource-efficient deployment of AI models in data-scarce environments, critical for edge computing and specialized applications.
AI models can now be optimized for specific tasks without significant accuracy degradation, reducing computational overhead and making AI more accessible.
- · Edge AI developers
- · Specialized AI applications
- · Companies with limited proprietary datasets
- · Hardware manufacturers for efficient AI
- · General-purpose, unoptimized AI deployment strategies
Improved performance and reduced resource consumption for fine-tuned CNNs in transfer learning scenarios.
Accelerated adoption of AI in industries with proprietary but small datasets, as model deployment becomes more feasible.
A shift towards more tailored and efficient AI solutions, potentially reducing the dominance of massive, generalized models in specific niches.
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Read at arXiv cs.LG