  Out-of-distribution (OOD) detection, crucial for reliable pattern
classification, discerns whether a sample originates outside the training
distribution. This paper concentrates on the high-dimensional features output
by the final convolutional layer, which contain rich image features. Our key
idea is to project these high-dimensional features into two specific feature
subspaces, leveraging the dimensionality reduction capacity of the network's
linear layers, trained with Predefined Evenly-Distribution Class Centroids
(PEDCC)-Loss. This involves calculating the cosines of three projection angles
and the norm values of features, thereby identifying distinctive information
for in-distribution (ID) and OOD data, which assists in OOD detection. Building
upon this, we have modified the batch normalization (BN) and ReLU layer
preceding the fully connected layer, diminishing their impact on the output
feature distributions and thereby widening the distribution gap between ID and
OOD data features. Our method requires only the training of the classification
network model, eschewing any need for input pre-processing or specific OOD data
pre-tuning. Extensive experiments on several benchmark datasets demonstrates
that our approach delivers state-of-the-art performance. Our code is available
at https://github.com/Hewell0/ProjOOD.
