A Two-Head Loss Function for Deep Average-K Classification

Camille Garcin, Maximilien Servajean, Alexis Joly, Joseph Salmon; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 7358-7367

Average-K classification is an alternative to top-K classification in which the number of labels returned varies with the ambiguity of the input image but must average to K over all the samples. A simple method to solve this task is to threshold the softmax output of a model trained with the cross-entropy loss. This approach is theoretically proven to be asymptotically consistent but it is not guaranteed to be optimal for a finite set of samples. In this paper we propose a new loss function based on a multi-label classification head in addition to the classical softmax. This second head is trained using pseudo-labels generated by thresholding the softmax head while guaranteeing that K classes are returned on average. We show that this approach allows the model to better capture ambiguities between classes and as a result to return more consistent sets of possible classes. Experiments on two datasets from the literature demonstrate that our approach outperforms the softmax baseline as well as several other loss functions more generally designed for weakly supervised multi-label classification. The gains are larger the higher the uncertainty especially for classes with few samples.