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Found 9 papers, 7 papers with code
PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scores
Recent advancements in audio-visual generative modeling have been propelled by progress in deep learning and the availability of data-rich benchmarks.
Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations
While end-to-end training of Deep Neural Networks (DNNs) yields state of the art performance in an increasing array of applications, it does not provide insight into, or control over, the features being extracted.
Self-supervised Speaker Recognition Training Using Human-Machine Dialogues
Speaker recognition, recognizing speaker identities based on voice alone, enables important downstream applications, such as personalization and authentication.
Sparse Coding Frontend for Robust Neural Networks
Deep Neural Networks are known to be vulnerable to small, adversarially crafted, perturbations.
A Neuro-Inspired Autoencoding Defense Against Adversarial Perturbations
Our nominal design is to train the decoder and classifier together in standard supervised fashion, but we also consider unsupervised decoder training based on a regression objective (as in a conventional autoencoder) with separate supervised training of the classifier.
Wireless Fingerprinting via Deep Learning: The Impact of Confounding Factors
The opportunity for doing so arises due to subtle nonlinear variations across transmitters, even those made by the same manufacturer.
Polarizing Front Ends for Robust CNNs
The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity."
Robust Wireless Fingerprinting via Complex-Valued Neural Networks
A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security.
Robust Adversarial Learning via Sparsifying Front Ends
We also devise attacks based on the locally linear model that outperform the well-known FGSM attack.
