  In this paper, we propose the Redundancy Reduction Twins Network (RRTN), a
redundancy reduction training framework that minimizes redundancy by measuring
the cross-correlation matrix between the outputs of the same network fed with
distorted versions of a sample and bringing it as close to the identity matrix
as possible. RRTN also applies a new loss function, the Barlow Twins loss
function, to help maximize the similarity of representations obtained from
different distorted versions of a sample. However, as the distribution of
losses can cause performance fluctuations in the network, we also propose the
use of a Restrained Uncertainty Weight Loss (RUWL) or joint training to
identify the best weights for the loss function. Our best approach on CNN14
with the proposed methodology obtains a CCC over emotion regression of 0.678 on
the ExVo Multi-task dev set, a 4.8% increase over a vanilla CNN 14 CCC of
0.647, which achieves a significant difference at the 95% confidence interval
(2-tailed).
