  Self supervision and natural language supervision have emerged as two
exciting ways to train general purpose image encoders which excel at a variety
of downstream tasks. Recent works such as M3AE and SLIP have suggested that
these approaches can be effectively combined, but most notably their results
use small pre-training datasets (<50M samples) and don't effectively reflect
the large-scale regime (>100M examples) that is commonly used for these
approaches. Here we investigate whether a similar approach can be effective
when trained with a much larger amount of data. We find that a combination of
two state of the art approaches: masked auto-encoders, MAE and contrastive
language image pre-training, CLIP provides a benefit over CLIP when trained on
a corpus of 11.3M image-text pairs, but little to no benefit (as evaluated on a
suite of common vision tasks) over CLIP when trained on a large corpus of 1.4B
images. Our work provides some much needed clarity into the effectiveness (or
lack thereof) of self supervision for large-scale image-text training.
