AMSGA extends Forward-Forward learning via multi-scale goodness aggregation, curriculum-guided hard negative mining, and adaptive thresholds, reporting up to 1.5% accuracy gains on MNIST and Fashion-MNIST.
Novel Saliency Analysis for the Forward-Forward Algo- rithm.2024 2nd International Conference On Artificial Intelligence, Blockchain, And Internet Of Things (AIBThings)
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Adaptive Multi-Scale Goodness Aggregation for Forward-Forward Learning
AMSGA extends Forward-Forward learning via multi-scale goodness aggregation, curriculum-guided hard negative mining, and adaptive thresholds, reporting up to 1.5% accuracy gains on MNIST and Fashion-MNIST.