7 research outputs found
Universum-inspired Supervised Contrastive Learning
As an effective data augmentation method, Mixup synthesizes an extra amount
of samples through linear interpolations. Despite its theoretical dependency on
data properties, Mixup reportedly performs well as a regularizer and calibrator
contributing reliable robustness and generalization to deep model training. In
this paper, inspired by Universum Learning which uses out-of-class samples to
assist the target tasks, we investigate Mixup from a largely under-explored
perspective - the potential to generate in-domain samples that belong to none
of the target classes, that is, universum. We find that in the framework of
supervised contrastive learning, Mixup-induced universum can serve as
surprisingly high-quality hard negatives, greatly relieving the need for large
batch sizes in contrastive learning. With these findings, we propose
Universum-inspired supervised Contrastive learning (UniCon), which incorporates
Mixup strategy to generate Mixup-induced universum as universum negatives and
pushes them apart from anchor samples of the target classes. We extend our
method to the unsupervised setting, proposing Unsupervised Universum-inspired
contrastive model (Un-Uni). Our approach not only improves Mixup with hard
labels, but also innovates a novel measure to generate universum data. With a
linear classifier on the learned representations, UniCon shows state-of-the-art
performance on various datasets. Specially, UniCon achieves 81.7% top-1
accuracy on CIFAR-100, surpassing the state of art by a significant margin of
5.2% with a much smaller batch size, typically, 256 in UniCon vs. 1024 in
SupCon using ResNet-50. Un-Uni also outperforms SOTA methods on CIFAR-100. The
code of this paper is released on https://github.com/hannaiiyanggit/UniCon.Comment: Accepted by IEEE Transactions on Image Processin
All Beings Are Equal in Open Set Recognition
In open-set recognition (OSR), a promising strategy is exploiting
pseudo-unknown data outside given known classes as an additional +-th
class to explicitly model potential open space. However, treating unknown
classes without distinction is unequal for them relative to known classes due
to the category-agnostic and scale-agnostic of the unknowns. This inevitably
not only disrupts the inherent distributions of unknown classes but also incurs
both class-wise and instance-wise imbalances between known and unknown classes.
Ideally, the OSR problem should model the whole class space as +,
but enumerating all unknowns is impractical. Since the core of OSR is to
effectively model the boundaries of known classes, this means just focusing on
the unknowns nearing the boundaries of targeted known classes seems sufficient.
Thus, as a compromise, we convert the open classes from infinite to , with a
novel concept Target-Aware Universum (TAU) and propose a simple yet effective
framework Dual Contrastive Learning with Target-Aware Universum (DCTAU). In
details, guided by the targeted known classes, TAU automatically expands the
unknown classes from the previous to , effectively alleviating the
distribution disruption and the imbalance issues mentioned above. Then, a novel
Dual Contrastive (DC) loss is designed, where all instances irrespective of
known or TAU are considered as positives to contrast with their respective
negatives. Experimental results indicate DCTAU sets a new state-of-the-art.Comment: Accepted by the main track The 38th Annual AAAI Conference on
Artificial Intelligence (AAAI 2024