  Self-supervised learning (SSL) models confront challenges of abrupt
informational collapse or slow dimensional collapse. We propose TriNet, which
introduces a novel triple-branch architecture for preventing collapse and
stabilizing the pre-training. TriNet learns the SSL latent embedding space and
incorporates it to a higher level space for predicting pseudo target vectors
generated by a frozen teacher. Our experimental results show that the proposed
method notably stabilizes and accelerates pre-training and achieves a relative
word error rate reduction (WERR) of 6.06% compared to the state-of-the-art
(SOTA) Data2vec for a downstream benchmark ASR task. We will release our code
at https://github.com/tencent-ailab/.
