Sparse Goodness: How Selective Measurement Transforms Forward-Forward Learning

This research systematically studies and enhances the Forward-Forward (FF) algorithm by redesigning its local goodness function, which distinguishes positive from negative data. It introduces 'top-k goodness' and 'entmax-weighted energy,' demonstrating substantial accuracy improvements on benchmarks like Fashion-MNIST.