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Tamasha Malepathirana

2 accepted papers

2024

When to Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks

AAAI 2024technical

Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks. One crucial aspect of neural growth is determining the optimal growth timing. However, few studies investigate this systematically. Our study re…

Cited by 1SourcePDFScholar
2023

NAPA-VQ: Neighborhood-Aware Prototype Augmentation with Vector Quantization for Continual Learning

ICCV 2023poster

Catastrophic forgetting; the loss of old knowledge upon acquiring new knowledge, is a pitfall faced by deep neural networks in real-world applications. Many prevailing solutions to this problem rely on storing exemplars (previously encountered data), which may not be feasible in applications with me…

Cited by 14PDFcodeScholar