  For safety-related applications, it is crucial to produce trustworthy deep
neural networks whose prediction is associated with confidence that can
represent the likelihood of correctness for subsequent decision-making.
Existing dense binary classification models are prone to being over-confident.
To improve model calibration, we propose Adaptive Stochastic Label Perturbation
(ASLP) which learns a unique label perturbation level for each training image.
ASLP employs our proposed Self-Calibrating Binary Cross Entropy (SC-BCE) loss,
which unifies label perturbation processes including stochastic approaches
(like DisturbLabel), and label smoothing, to correct calibration while
maintaining classification rates. ASLP follows Maximum Entropy Inference of
classic statistical mechanics to maximise prediction entropy with respect to
missing information. It performs this while: (1) preserving classification
accuracy on known data as a conservative solution, or (2) specifically improves
model calibration degree by minimising the gap between the prediction accuracy
and expected confidence of the target training label. Extensive results
demonstrate that ASLP can significantly improve calibration degrees of dense
binary classification models on both in-distribution and out-of-distribution
data. The code is available on https://github.com/Carlisle-Liu/ASLP.
