  We introduce a novel validation framework to measure the true robustness of
learning models for real-world applications by creating source-inclusive and
source-exclusive partitions in a dataset via clustering. We develop a
robustness metric derived from source-aware lower and upper bounds of model
accuracy even when data source labels are not readily available. We clearly
demonstrate that even on a well-explored dataset like MNIST, challenging
training scenarios can be constructed under the proposed assessment framework
for two separate yet equally important applications: i) more rigorous learning
model comparison and ii) dataset adequacy evaluation. In addition, our findings
not only promise a more complete identification of trade-offs between model
complexity, accuracy and robustness but can also help researchers optimize
their efforts in data collection by identifying the less robust and more
challenging class labels.
