Publications associées à Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

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The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

4M: Massively Multimodal Masked Modeling

Interpretable Representation Learning and Evaluation for Abstractive Summarization

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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

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Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration

4M: Massively Multimodal Masked Modeling

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

Interpretable Representation Learning and Evaluation for Abstractive Summarization

Discriminative Deep Feature Visualization for Explainable Face Recognition

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Fast Adversarial Training With Adaptive Step Size

Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision

Fashioning the Future: Unlocking the Creative Potential of Deep Generative Models for Design Space Exploration