Deep neural network calibration by reducing classifier shift with stochastic masking

Key Points

• Calibration of deep neural networks is enhanced by reducing classifier shift using stochastic masking.
• The study found a notable improvement of 25% in classification accuracy after applying the new method.
• This approach involves a new technique called stochastic masking to tackle classifier shift.
• The findings support the potential for better machine learning models with improved calibration strategies.