Conceitos essenciais
The author explores implicit regularization effects in multi-task learning and fine-tuning, highlighting biases towards feature sharing and sparse task-specific feature learning. The study uncovers a novel nested feature selection regime that enhances performance through sparsity.
Resumo
The study delves into the inductive biases of multi-task learning (MTL) and pretraining with subsequent fine-tuning (PT+FT) in neural networks. It reveals how these strategies incentivize feature reuse and sparse task-specific feature learning. The research identifies a unique nested feature selection regime that promotes sparsity within features inherited from pretraining, leading to improved performance. By conducting experiments with linear and ReLU networks, the study validates theoretical predictions and provides practical insights for optimizing training strategies.
Key points:
- Investigates implicit regularization effects in MTL and PT+FT.
- Identifies biases towards feature sharing and sparse task-specific features.
- Introduces a novel nested feature selection regime enhancing network performance.
- Validates findings through experiments with linear and ReLU networks.
Estatísticas
In MTL, a solution minimizes an ℓ1,2 penalty incentivizing group sparsity on the learned linear map β.
For PT+FT, there is a hybrid of "rich" and "lazy" learning dynamics due to suitable parameter scalings.
The PT+FT penalty encourages reusing features from the auxiliary task based on their weights.
Citações
"In this work we characterize the inductive biases of MTL and PT+FT in terms of implicit regularization."
"Our findings shed light on the impact of auxiliary task learning and suggest ways to leverage it more effectively."