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Found 11 papers, 7 papers with code
Benchmarking Robustness to Adversarial Image Obfuscations
We evaluate 33 pretrained models on the benchmark and train models with different augmentations, architectures and training methods on subsets of the obfuscations to measure generalization.
Data Augmentation Can Improve Robustness
Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training.
A Fine-Grained Analysis on Distribution Shift
Despite this necessity, there has been little work in defining the underlying mechanisms that cause these shifts and evaluating the robustness of algorithms across multiple, different distribution shifts.
Improving Robustness using Generated Data
Against $\ell_\infty$ norm-bounded perturbations of size $\epsilon = 8/255$, our models achieve 66. 10% and 33. 49% robust accuracy on CIFAR-10 and CIFAR-100, respectively (improving upon the state-of-the-art by +8. 96% and +3. 29%).
Defending Against Image Corruptions Through Adversarial Augmentations
Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog.
Fixing Data Augmentation to Improve Adversarial Robustness
In particular, against $\ell_\infty$ norm-bounded perturbations of size $\epsilon = 8/255$, our model reaches 64. 20% robust accuracy without using any external data, beating most prior works that use external data.
Efficient Neural Audio Synthesis
The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time.
Wavenet based low rate speech coding
Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used.
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system.
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
We introduce a model where the rate of an inhomogeneous Poisson process is modified by a Chinese restaurant process.
Inference in continuous-time change-point models
We consider the problem of Bayesian inference for continuous time multi-stable stochastic systems which can change both their diffusion and drift parameters at discrete times.
