  Large pre-trained models, such as Bert, GPT, and Wav2Vec, have demonstrated
great potential for learning representations that are transferable to a wide
variety of downstream tasks . It is difficult to obtain a large quantity of
supervised data due to the limited availability of resources and time. In light
of this, a significant amount of research has been conducted in the area of
adopting large pre-trained datasets for diverse downstream tasks via fine
tuning, linear probing, or prompt tuning in low resource settings.
Normalization techniques are essential for accelerating training and improving
the generalization of deep neural networks and have been successfully used in a
wide variety of applications. A lot of normalization techniques have been
proposed but the success of normalization in low resource downstream NLP and
speech tasks is limited. One of the reasons is the inability to capture
expressiveness by rescaling parameters of normalization. We propose
KullbackLeibler(KL) Regularized normalization (KL-Norm) which make the
normalized data well behaved and helps in better generalization as it reduces
over-fitting, generalises well on out of domain distributions and removes
irrelevant biases and features with negligible increase in model parameters and
memory overheads. Detailed experimental evaluation on multiple low resource NLP
and speech tasks, demonstrates the superior performance of KL-Norm as compared
to other popular normalization and regularization techniques.
