Continual learning has received a great deal of attention recently with several approaches being proposed.
We propose a deep collaborative weight-based classification (DeepCWC) method to resolve this problem, by providing a novel option to fully take advantage of deep features in classic machine learning.
We propose a smooth kernel regularizer that encourages spatial correlations in convolution kernel weights.
We have developed convolutional neural networks (CNN) for a facial expression recognition task.
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.
This paper identifies a problem with the usual procedure for L2-regularization parameter estimation in a domain adaptation setting.