  The mainstream AI community has seen a rise in large-scale open-source
classifiers, often pre-trained on vast datasets and tested on standard
benchmarks; however, users facing diverse needs and limited, expensive test
data may be overwhelmed by available choices. Deep Neural Network (DNN)
classifiers undergo training, validation, and testing phases using example
dataset, with the testing phase focused on determining the classification
accuracy of test examples without delving into the inner working of the
classifier. In this work, we evaluate a DNN classifier's training quality
without any example dataset. It is assumed that a DNN is a composition of a
feature extractor and a classifier which is the penultimate completely
connected layer. The quality of a classifier is estimated using its weight
vectors. The feature extractor is characterized using two metrics that utilize
feature vectors it produces when synthetic data is fed as input. These
synthetic input vectors are produced by backpropagating desired outputs of the
classifier. Our empirical study of the proposed method for ResNet18, trained
with CAFIR10 and CAFIR100 datasets, confirms that data-less evaluation of DNN
classifiers is indeed possible.
