Adaptive Label Smoothing

Understand

This paper concerns the use of objectness measures to improve the calibration performance of Convolutional Neural Networks (CNNs).

• CNNs have proven to be very good classifiers and generally localize objects well; however, the loss functions typically used to train classification CNNs do not penalize inability to localize an object, nor do they take into account an object's relative size in the given image.
• During training on ImageNet-1K almost all approaches use random crops on the images and this transformation sometimes provides the CNN with background only samples.
• This causes the classifiers to depend on context.