4 papers
Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics
Hamidreza Yaghoubi Araghi, Parastoo Pilevar, Ming C. Lin
Robotic perception pipelines increasingly rely on large vision backbones deployed on SWaP-constrained edge platforms, making post-training quantization (PTQ) attractive for real-ti…
Quantifying and Modeling Driving Styles in Trajectory Forecasting
Laura Zheng, Hamidreza Yaghoubi Araghi, Tony Wu +3
Trajectory forecasting has become a popular deep learning task due to its relevance for scenario simulation for autonomous driving. Specifically, trajectory forecasting predicts th…
Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation
Fahimeh Hosseini Noohdani, Parsa Hosseini, Aryan Yazdan Parast +2
While standard Empirical Risk Minimization (ERM) training is proven effective for image classification on in-distribution data, it fails to perform well on out-of-distribution samp…
Annotation-Free Group Robustness via Loss-Based Resampling
Mahdi Ghaznavi, Hesam Asadollahzadeh, HamidReza Yaghoubi Araghi +3
It is well-known that training neural networks for image classification with empirical risk minimization (ERM) makes them vulnerable to relying on spurious attributes instead of ca…