  We study the impact of different pruning techniques on the representation
learned by deep neural networks trained with contrastive loss functions. Our
work finds that at high sparsity levels, contrastive learning results in a
higher number of misclassified examples relative to models trained with
traditional cross-entropy loss. To understand this pronounced difference, we
use metrics such as the number of PIEs (Hooker et al., 2019), Q-Score (Kalibhat
et al., 2022), and PD-Score (Baldock et al., 2021) to measure the impact of
pruning on the learned representation quality. Our analysis suggests the
schedule of the pruning method implementation matters. We find that the
negative impact of sparsity on the quality of the learned representation is the
highest when pruning is introduced early on in the training phase.
