  Different self-supervised tasks (SSL) reveal different features from the
data. The learned feature representations can exhibit different performance for
each downstream task. In this light, this work aims to combine Multiple SSL
tasks (Multi-SSL) that generalizes well for all downstream tasks. Specifically,
for this study, we investigate binaural sounds and image data in isolation. For
binaural sounds, we propose three SSL tasks namely, spatial alignment, temporal
synchronization of foreground objects and binaural audio and temporal gap
prediction. We investigate several approaches of Multi-SSL and give insights
into the downstream task performance on video retrieval, spatial sound super
resolution, and semantic prediction on the OmniAudio dataset. Our experiments
on binaural sound representations demonstrate that Multi-SSL via incremental
learning (IL) of SSL tasks outperforms single SSL task models and fully
supervised models in the downstream task performance. As a check of
applicability on other modality, we also formulate our Multi-SSL models for
image representation learning and we use the recently proposed SSL tasks,
MoCov2 and DenseCL. Here, Multi-SSL surpasses recent methods such as MoCov2,
DenseCL and DetCo by 2.06%, 3.27% and 1.19% on VOC07 classification and +2.83,
+1.56 and +1.61 AP on COCO detection. Code will be made publicly available.
