  Disentangled visual representations have largely been studied with generative
models such as Variational AutoEncoders (VAEs). While prior work has focused on
generative methods for disentangled representation learning, these approaches
do not scale to large datasets due to current limitations of generative models.
Instead, we explore regularization methods with contrastive learning, which
could result in disentangled representations that are powerful enough for large
scale datasets and downstream applications. However, we find that unsupervised
disentanglement is difficult to achieve due to optimization and initialization
sensitivity, with trade-offs in task performance. We evaluate disentanglement
with downstream tasks, analyze the benefits and disadvantages of each
regularization used, and discuss future directions.
