ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification

Understand

Contrastive learning has led to substantial improvements in the quality of learned embedding representations for tasks such as image classification.

• However, a key drawback of existing contrastive augmentation methods is that they may lead to the modification of the image content which can yield undesired alterations of its semantics.
• This can affect the performance of the model on downstream tasks.
• Hence, in this paper, we ask whether we can augment image data in contrastive learning such that the task-relevant semantic content of an image is preserved.