  Mixup is a widely adopted strategy for training deep networks, where
additional samples are augmented by interpolating inputs and labels of training
pairs. Mixup has shown to improve classification performance, network
calibration, and out-of-distribution generalisation. While effective, a
cornerstone of Mixup, namely that networks learn linear behaviour patterns
between classes, is only indirectly enforced since the output interpolation is
performed at the probability level. This paper seeks to address this limitation
by mixing the classifiers directly instead of mixing the labels for each mixed
pair. We propose to define the target of each augmented sample as a uniquely
new classifier, whose parameters are a linear interpolation of the classifier
vectors of the input pair. The space of all possible classifiers is continuous
and spans all interpolations between classifier pairs. To make optimisation
tractable, we propose a dual-contrastive Infinite Class Mixup loss, where we
contrast the classifier of a mixed pair to both the classifiers and the
predicted outputs of other mixed pairs in a batch. Infinite Class Mixup is
generic in nature and applies to many variants of Mixup. Empirically, we show
that it outperforms standard Mixup and variants such as RegMixup and Remix on
balanced, long-tailed, and data-constrained benchmarks, highlighting its broad
applicability.
