Oceania
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
Currently, the data augmentation parameters are chosen by human effort and costly cross-validation, which makes it cumbersome to apply to new datasets. We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network.
Supplementary Material I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification
Our novel I2DFormer which only utilizes the I2DAttention module, outperforms all these baselines in row d). Our model is designed with the problem constraints of our ZSL setting and the resulting information asymmetry in mind.
Improved Feature Distillation via Projector Ensemble Yudong Chen 1 Sen Wang
We observe that the student network benefits from a projector even if the feature dimensions of the student and the teacher are the same.
