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Adversarial Robustness in Event-Based Neural Networks
The project will focus on studying various neural network architectures for event-based inference datasets and evaluate their performance in the presence of adversarial attacks.
The robustness and reliability of neural networks are of utmost importance in several computer vision applications, especially in automotive applications where real-time predictions are crucial for safe and efficient operation. In this context, event-based cameras, due to their unique property of capturing changes in the scene, have shown impressive performance in low-latency prediction tasks such as object detection, tracking, and optical flow prediction. However, in order to be widely adopted in the real world, the robustness and reliability of such event-based networks have to be properly studied and verified. Until now, however, these aspects have been overlooked in the event-based literature. We look for students with strong programming (Pyhton/Matlab) and computer vision background. Additionally, knowledge in machine learning frameworks (pytorch, tensorflow) is required.
The robustness and reliability of neural networks are of utmost importance in several computer vision applications, especially in automotive applications where real-time predictions are crucial for safe and efficient operation. In this context, event-based cameras, due to their unique property of capturing changes in the scene, have shown impressive performance in low-latency prediction tasks such as object detection, tracking, and optical flow prediction. However, in order to be widely adopted in the real world, the robustness and reliability of such event-based networks have to be properly studied and verified. Until now, however, these aspects have been overlooked in the event-based literature. We look for students with strong programming (Pyhton/Matlab) and computer vision background. Additionally, knowledge in machine learning frameworks (pytorch, tensorflow) is required.
The project will focus on studying various neural network architectures for event-based inference datasets and evaluate their performance in the presence of adversarial attacks.
The project will focus on studying various neural network architectures for event-based inference datasets and evaluate their performance in the presence of adversarial attacks.