arXiv:2601.15212v2 Announce Type: replace Abstract: Training deep computer vision models requires manual oversight or hyperparameter tuning of the learning rate (LR) schedule. While existing adaptive optimizers schedule the LR automatically, they suffer from computational and memory overhead, incompatibility with regularization, and suboptimal LR choices. In this work, we introduce the ZENITH (Zero-overhead Evolution using Norm-Informed Training History) optimizer, which adapts the LR using the temporal evolution of the gradient norm. Image classification experiments spanning 6 CNN architectur
The continuous drive for more efficient and automated deep learning training methods is addressing current computational and human oversight limitations.
Improved optimizer efficiency can accelerate AI development, reduce computational costs, and make advanced models more accessible, especially for computer vision applications.
Deep learning model training, particularly for computer vision, could become significantly more autonomous and less reliant on manual hyperparameter tuning, leading to faster research cycles and deployment.
- · AI researchers
- · Deep learning practitioners
- · Cloud computing providers (through increased efficiency)
- · Companies deploying computer vision models
- · Manual hyperparameter tuning consultancies
- · Inefficient AI training practices
Reduced time and cost for training complex deep learning computer vision models.
Faster iteration cycles in AI development, potentially accelerating breakthroughs and application deployments.
Lower barriers to entry for AI model development, democratizing access to powerful computer vision capabilities.
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