Title: Transferring Inductive Biases through Knowledge Distillation. Abstract: Having the right inductive biases can be crucial in many tasks or scenarios where data or computing resources are a limiting factor, or where training data is not perfectly representative of the conditions at test time. However, defining, designing, and efficiently adapting inductive biases is not necessarily straightforward. Inductive biases of a model affect its generalisation behaviour and influence the solution it converges to from different aspects. In this paper, we investigate the power of knowledge distillation in transferring the effects of inductive biases of a teacher model to a student model, when they have different architectures.
We consider different families of models: LSTMs vs. Transformers and CNNs vs. MLPs, in the context of tasks and scenarios with linguistics and vision applications, where having the right inductive biases is critical. We train our models in different setups: no knowledge distillation, self-distillation, and distillation using a teacher with a better inductive bias for the task at hand. We show that in the later setup, compared to no distillation and self-distillation, we can not only improve the performance of the students, but also the solutions they converge become similar to their teachers with respect to a wide range of properties, including different task-specific performance metrics, per sample behaviour of the models, representational similarity and how the representational space of the models evolve during training, performance on out-of-distribution datasets, confidence calibration, and finally whether the converged solutions fall within the same basins of attractions.