Rényi Divergence Deep Mutual Learning
This is an incremental improvement for machine learning practitioners using DML, offering a tunable alternative with theoretical convergence guarantees.
The paper tackles the problem of improving Deep Mutual Learning (DML) by replacing KL divergence with Rényi divergence, resulting in consistent performance gains and enhanced model generalization.
This paper revisits Deep Mutual Learning (DML), a simple yet effective computing paradigm. We propose using Rényi divergence instead of the KL divergence, which is more flexible and tunable, to improve vanilla DML. This modification is able to consistently improve performance over vanilla DML with limited additional complexity. The convergence properties of the proposed paradigm are analyzed theoretically, and Stochastic Gradient Descent with a constant learning rate is shown to converge with $\mathcal{O}(1)$-bias in the worst case scenario for nonconvex optimization tasks. That is, learning will reach nearby local optima but continue searching within a bounded scope, which may help mitigate overfitting. Finally, our extensive empirical results demonstrate the advantage of combining DML and Rényi divergence, leading to further improvement in model generalization.