A Tensor Train decomposition-based method enables efficient gradient-free activation maximization for neurons in spiking neural networks by searching generative model latent spaces.
Learning multiple layers of features from tiny images
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RECALL achieves rehearsal-free continual learning for object classification by logit recall before new training, regression regularization, Mahalanobis loss on known categories, and new heads per sequence, outperforming prior methods on CORe50, iCIFAR-100, and the introduced HOWS-CL-25 dataset.
ORDAC adaptively corrects noisy ordinal labels via dynamic label distribution adjustments, yielding lower error and higher recall on noisy Adience and Diabetic Retinopathy benchmarks.
The paper delivers a two-level hierarchical classification of edge case detection methods in automated driving, covering AV modules and methodologies, plus evaluation metrics and open challenges.
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Edge Case Detection in Automated Driving: Methods, Challenges and Future Directions
The paper delivers a two-level hierarchical classification of edge case detection methods in automated driving, covering AV modules and methodologies, plus evaluation metrics and open challenges.