Towards Robust Feature Learning with t-vFM Similarity for Continual
  Learning
Bilan Gao YoungBin Kim
Continual learning has been developed using standard supervised contrastive
loss from the perspective of feature learning. Due to the data imbalance during
the training, there are still challenges in learning better representations. In
this work, we suggest using a different similarity metric instead of cosine
similarity in supervised contrastive loss in order to learn more robust
representations. We validate the our method on one of the image classification
datasets Seq-CIFAR-10 and the results outperform recent continual learning
baselines.