  Transformers have gained increasing popularity in a wide range of
applications, including Natural Language Processing (NLP), Computer Vision and
Speech Recognition, because of their powerful representational capacity.
However, harnessing this representational capacity effectively requires a large
amount of data, strong regularization, or both, to mitigate overfitting.
Recently, the power of the Transformer has been unlocked by self-supervised
pretraining strategies based on masked autoencoders which rely on
reconstructing masked inputs, directly, or contrastively from unmasked content.
This pretraining strategy which has been used in BERT models in NLP, Wav2Vec
models in Speech and, recently, in MAE models in Vision, forces the model to
learn about relationships between the content in different parts of the input
using autoencoding related objectives. In this paper, we propose a novel, but
surprisingly simple alternative to content reconstruction~-- that of predicting
locations from content, without providing positional information for it. Doing
so requires the Transformer to understand the positional relationships between
different parts of the input, from their content alone. This amounts to an
efficient implementation where the pretext task is a classification problem
among all possible positions for each input token. We experiment on both Vision
and Speech benchmarks, where our approach brings improvements over strong
supervised training baselines and is comparable to modern
unsupervised/self-supervised pretraining methods. Our method also enables
Transformers trained without position embeddings to outperform ones trained
with full position information.
