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Found 5 papers, 2 papers with code
Layerwise Early Stopping for Test Time Adaptation
In this paper, we propose Layerwise EArly STopping (LEAST) for TTA to address this problem.
GROOD: GRadient-aware Out-Of-Distribution detection in interpolated manifolds
To build this space, GROOD relies on class prototypes together with a prototype that specifically captures OOD characteristics.
Hessian Aware Low-Rank Weight Perturbation for Continual Learning
We conduct extensive experiments on various benchmarks, including a dataset with large-scale tasks, and compare our method against some recent state-of-the-art methods to demonstrate the effectiveness and scalability of our proposed method.
Differentiable SLAM Helps Deep Learning-based LiDAR Perception Tasks
We investigate a new paradigm that uses differentiable SLAM architectures in a self-supervised manner to train end-to-end deep learning models in various LiDAR based applications.
DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder
We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments.
