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One Size Doesn't Fit All: Adaptive Label Smoothing

This paper concerns the use of objectness measures to improve the calibration performance of Convolutional Neural Networks (CNNs). Objectness is a measure of likelihood of an object from \textit{any} class being present in a given image. 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 with the correct label,this causes the classifiers to overfit to context. Context dependence is harmful for safety-critical applications. We present a novel approach to object localization that combines the ideas of objectness and label smoothing during training. Unlike previous methods, we compute a smoothing factor that is \emph{adaptive} based on relative object size within an image. This causes our approach to produce confidences that are grounded in the size of the object being classified instead of relying on context to make the correct predictions. We present extensive results using ImageNet to demonstrate that CNNs trained using adaptive label smoothing are much less likely to be overconfident in their predictions, as compared to CNNs trained using hard targets. We also show qualitative results using class activation maps to illustrate the improvements.

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