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  • 2018

  • Triantafyllopoulos, Andreas; Sagha, Hesam; Eyben, Florian; Schuller, Björn (2018): audEERING's approach to the One-Minute-Gradual Emotion Challenge. Online verfügbar unter https://arxiv.org/pdf/1805.01222

     

    Abstract: This paper describes audEERING's submissions as well as additional evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge. We provide the results for audio and video processing on subject (in)dependent evaluations. On the provided Development set, we achieved 0.343 Concordance Correlation Coefficient (CCC) for arousal (from audio) and .401 for valence (from video).

    Keywords: Audio and Speech Processing (eess.AS), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), Machine Learning (stat.ML), Sound (cs.SD)
  • Triantafyllopoulos, Andreas; Sagha, Hesam; Eyben, Florian; Schuller, Björn (2018): audEERING's approach to the One-Minute-Gradual Emotion Challenge. Online verfügbar unter https://arxiv.org/pdf/1805.01222

     

    Abstract: This paper describes audEERING's submissions as well as additional evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge. We provide the results for audio and video processing on subject (in)dependent evaluations. On the provided Development set, we achieved 0.343 Concordance Correlation Coefficient (CCC) for arousal (from audio) and .401 for valence (from video).

    Keywords: Audio and Speech Processing (eess.AS), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), Machine Learning (stat.ML), Sound (cs.SD)

  • 2014

  • Goodfellow, Ian; Shlens, Jonathon; Szegedy, Christian (2014): Explaining and Harnessing Adversarial Examples. In: ICLR 2015. Online verfügbar unter https://arxiv.org/pdf/1412.6572

     

    Abstract: Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.

    Keywords: Machine Learning (cs.LG), Machine Learning (stat.ML)
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