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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Fast gradient-free activation maximization for neurons in spiking neural networks
A Tensor Train decomposition-based method enables efficient gradient-free activation maximization for neurons in spiking neural networks by searching generative model latent spaces.
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RECALL: Rehearsal-free Continual Learning for Object Classification
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.
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Ordinal Adaptive Correction: A Data-Centric Approach to Ordinal Image Classification with Noisy Labels
ORDAC adaptively corrects noisy ordinal labels via dynamic label distribution adjustments, yielding lower error and higher recall on noisy Adience and Diabetic Retinopathy benchmarks.
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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.