arXiv:2503.08038v2 Announce Type: replace-cross Abstract: In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists of (1) a weighted Mean Square Error (wMSE) loss and (2) a Cross-Entropy loss incorporating soft labels. Thanks to the decoupled structure of DKL loss, we have identified two areas for improvement. Firstly, we address the limitation of KL loss in scenarios like knowledge distillation by breaking its asymmetric optimization property along with a s
The continuous evolution of deep learning architectures and the push for more efficient and robust training methods make research into fundamental loss functions timely.
Improved loss functions like the Generalized Kullback-Leibler Divergence Loss can lead to more stable and effective training of AI models, particularly in complex tasks like knowledge distillation.
This research provides a more principled understanding and potential improvements for a widely used loss function, enabling better performance in various AI applications.
- · AI researchers and practitioners
- · Companies utilizing knowledge distillation for model compression
- · Developers of deep learning frameworks
- · Less efficient or unstable AI models
- · Applications bottlenecked by current knowledge distillation methods
The new DKL loss structure allows for more targeted optimization strategies in AI model training.
This could lead to a wave of more robust and performant AI models in diverse fields.
Enhanced AI model performance might accelerate breakthroughs in complex scientific simulations and AI agent capabilities.
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Read at arXiv cs.AI