  Neural networks are notorious for being overconfident predictors, posing a
significant challenge to their safe deployment in real-world applications.
While feature normalization has garnered considerable attention within the deep
learning literature, current train-time regularization methods for
Out-of-Distribution(OOD) detection are yet to fully exploit this potential.
Indeed, the naive incorporation of feature normalization within neural networks
does not guarantee substantial improvement in OOD detection performance. In
this work, we introduce T2FNorm, a novel approach to transforming features to
hyperspherical space during training, while employing non-transformed space for
OOD-scoring purposes. This method yields a surprising enhancement in OOD
detection capabilities without compromising model accuracy in
in-distribution(ID). Our investigation demonstrates that the proposed technique
substantially diminishes the norm of the features of all samples, more so in
the case of out-of-distribution samples, thereby addressing the prevalent
concern of overconfidence in neural networks. The proposed method also
significantly improves various post-hoc OOD detection methods.
