Saddle-to-saddle dynamics can improve transfer learning efficiency by selecting optimal initializations that emphasize simplicity bias.

Motivation

While the paper explores simplicity bias in neural networks during training, its implications for transfer learning, where initial weights can significantly impact learning efficiency and final performance, are underexplored. Understanding how saddle-to-saddle dynamics affect the choice of initial weights could optimize transfer learning processes.

Proposed Method

Design experiments where neural networks are pre-trained on a source task and then fine-tuned on a target task with varying initial weights derived from saddle-to-saddle dynamics. Compare the performance and convergence rates against standard transfer learning techniques using benchmarks like ImageNet and CIFAR-100.

Expected Contribution

This could reveal new strategies for selecting initial weights in transfer learning, potentially improving the efficiency and effectiveness of model adaptation across tasks.

Required Resources

Access to large-scale datasets such as ImageNet, computational resources for extensive training, and expertise in transfer learning techniques.

Source Paper

Saddle-to-Saddle Dynamics Explains A Simplicity Bias Across Neural Network Architectures

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