Mark N. Wegman

Adrian Shuai Li, Elisa Bertino, Xuan-Hong Dang et al.

We propose MaxDIRep, a domain adaptation method that improves the decomposition of data representations into domain-independent and domain-dependent components. Existing methods, such as Domain-Separation Networks (DSN), use a weak orthogonality constraint between these components, which can lead to label-relevant features being partially encoded in the domain-dependent representation (DDRep) rather than the domain-independent representation (DIRep). As a result, information crucial for target-domain classification may be missing from the DIRep. MaxDIRep addresses this issue by applying a Kullback-Leibler (KL) divergence constraint to minimize the information content of the DDRep, thereby encouraging the DIRep to retain features that are both domain-invariant and predictive of target labels. Through geometric analysis and an ablation study on synthetic datasets, we show why DSN's weaker constraint can lead to suboptimal adaptation. Experiments on standard image benchmarks and a network intrusion detection task demonstrate that MaxDIRep achieves strong performance, works with pretrained models, and generalizes to non-image classification tasks.

Haifeng Qian, Mark N. Wegman

This paper proposes a class of well-conditioned neural networks in which a unit amount of change in the inputs causes at most a unit amount of change in the outputs or any of the internal layers. We develop the known methodology of controlling Lipschitz constants to realize its full potential in maximizing robustness, with a new regularization scheme for linear layers, new ways to adapt nonlinearities and a new loss function. With MNIST and CIFAR-10 classifiers, we demonstrate a number of advantages. Without needing any adversarial training, the proposed classifiers exceed the state of the art in robustness against white-box L2-bounded adversarial attacks. They generalize better than ordinary networks from noisy data with partially random labels. Their outputs are quantitatively meaningful and indicate levels of confidence and generalization, among other desirable properties.