  Self-supervised learning (SSL) has made enormous progress and largely
narrowed the gap with the supervised ones, where the representation learning is
mainly guided by a projection into an embedding space. During the projection,
current methods simply adopt uniform aggregation of pixels for embedding;
however, this risks involving object-irrelevant nuisances and spatial
misalignment for different augmentations. In this paper, we present a new
approach, Learning Where to Learn (LEWEL), to adaptively aggregate spatial
information of features, so that the projected embeddings could be exactly
aligned and thus guide the feature learning better. Concretely, we reinterpret
the projection head in SSL as a per-pixel projection and predict a set of
spatial alignment maps from the original features by this weight-sharing
projection head. A spectrum of aligned embeddings is thus obtained by
aggregating the features with spatial weighting according to these alignment
maps. As a result of this adaptive alignment, we observe substantial
improvements on both image-level prediction and dense prediction at the same
time: LEWEL improves MoCov2 by 1.6%/1.3%/0.5%/0.4% points, improves BYOL by
1.3%/1.3%/0.7%/0.6% points, on ImageNet linear/semi-supervised classification,
Pascal VOC semantic segmentation, and object detection, respectively.
