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This paper explores methods to mitigate sleep deprivation effects in the Forward-Forward algorithm, showing accuracy improvements of 2%-62% on MNIST and Fashion-MNIST datasets through techniques like alternative activation and loss optimization.
This paper introduces a learnable channel-class assignment mechanism for forward-only convolutional neural networks, combined with entropy and orthogonality regularization and a loss-aware layer contribution strategy. The method achieves state-of-the-art performance among forward-forward algorithms on CIFAR-10, CIFAR-100, and Tiny-ImageNet, significantly narrowing the gap with backpropagation.