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Found 12 papers, 3 papers with code
Imbalance in Regression Datasets
For classification, the problem of class imbalance is well known and has been extensively studied.
Physical Adversarial Examples for Multi-Camera Systems
Neural networks build the foundation of several intelligent systems, which, however, are known to be easily fooled by adversarial examples.
Protecting Publicly Available Data With Machine Learning Shortcuts
Machine-learning (ML) shortcuts or spurious correlations are artifacts in datasets that lead to very good training and test performance but severely limit the model's generalization capability.
Complex-valued neural networks for voice anti-spoofing
Current anti-spoofing and audio deepfake detection systems use either magnitude spectrogram-based features (such as CQT or Melspectrograms) or raw audio processed through convolution or sinc-layers.
Shortcut Detection with Variational Autoencoders
For real-world applications of machine learning (ML), it is essential that models make predictions based on well-generalizing features rather than spurious correlations in the data.
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient
Neural networks follow a gradient-based learning scheme, adapting their mapping parameters by back-propagating the output loss.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
Visualizing Automatic Speech Recognition -- Means for a Better Understanding?
Automatic speech recognition (ASR) is improving ever more at mimicking human speech processing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning
An important problem in deep learning is the privacy and security of neural networks (NNs).
Optimizing Information Loss Towards Robust Neural Networks
To overcome the downsides of adversarial training while still providing a high level of security, we present a new training approach we call \textit{entropic retraining}.
$\text{A}^3$: Activation Anomaly Analysis
Based on the activation values in the target network, the alarm network decides if the given sample is normal.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
DLA: Dense-Layer-Analysis for Adversarial Example Detection
In this paper, we present a novel end-to-end framework to detect such attacks during classification without influencing the target model's performance.
