We propose two new regularizers using pseudo samples both on and off the data manifold to mitigate data scarcity and prevent over-confident predictions.
our method imposes two types of regularization for better calibration during fine-tuning: (1) On-manifold regularization: We first generate on-manifold samples by interpolating the training data and their corresponding labels along the direction learned from hidden feature space; training over such augmented on-manifold data introduces a smoothness constraint within the data manifold to improve the model calibration for in-distribution data.