… Recently, deep supervision has been proposed to add auxiliary classifiers … deep neural
networks. By optimizing these auxiliary classifiers with the supervised task loss, the supervision …

… The cross-entropy loss is the most widely used loss function for supervised learning of deep
classification models. A number of works have explored shortcomings of this loss, such as …

… Abstract—Deep supervised learning has achieved great success in the last decade. However,
its defects of heavy dependence on manual labels and vulnerability to attacks have driven …

… approaches to deep multi-… supervision-based methods for deep MVC, potentially slowing
the progress of the field. To address this, we present DeepMVC, a unified framework for deep …

… the widespread supervision among the … -text contrastive supervision, we fully exploit data
potential through the use of (1) self-supervision within each modality; (2) multi-view supervision …

… Similar to other deep learning methods, contrastive learning employs a variety of optimization
algorithms for training. The training process involves learning the parameters of encoder …

… -art by relying on the contrastive learning paradigm, which learns … Our work analyzes
contrastive learning without assuming … provable analysis for contrastive learning where …

… learning is a research direction that delved into the vulnerability of deep networks in the
intrinsic representation space, which we believe is the root cause of fragility of existing …

… In the last decade, deep supervised learning has had tremendous success. However, its …
Incorporating contrastive learning (CL) for self-supervised learning (SSL) has turned out as …

Deep learning on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However, precise …