Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond
Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …
Trustworthy AI: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …
applications, but the outcomes of many AI models are challenging to comprehend and trust …
Mat: Mask-aware transformer for large hole image inpainting
Recent studies have shown the importance of modeling long-range interactions in the
inpainting problem. To achieve this goal, existing approaches exploit either standalone …
inpainting problem. To achieve this goal, existing approaches exploit either standalone …
Resolution-robust large mask inpainting with fourier convolutions
R Suvorov, E Logacheva, A Mashikhin… - Proceedings of the …, 2022 - openaccess.thecvf.com
Modern image inpainting systems, despite the significant progress, often struggle with large
missing areas, complex geometric structures, and high-resolution images. We find that one …
missing areas, complex geometric structures, and high-resolution images. We find that one …
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Deep learning has been shown to be an effective tool in solving partial differential equations
(PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual …
(PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual …
Knowledge distillation: A survey
In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …
especially for computer vision tasks. The great success of deep learning is mainly due to its …
Adversarial weight perturbation helps robust generalization
The study on improving the robustness of deep neural networks against adversarial
examples grows rapidly in recent years. Among them, adversarial training is the most …
examples grows rapidly in recent years. Among them, adversarial training is the most …
Toward transparent ai: A survey on interpreting the inner structures of deep neural networks
The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …
Improving adversarial robustness requires revisiting misclassified examples
Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by
imperceptible perturbations. A range of defense techniques have been proposed to improve …
imperceptible perturbations. A range of defense techniques have been proposed to improve …