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Revisiting Gradient Descent: A Dual-Weight Method for Improved Learning
Xi Wang
We introduce a novel framework for learning in neural networks by decomposing each neuron's weight vector into two distinct parts, W₁ and W₂, thereby modeling contrastive information directly at the neuron level. Traditional gradient descent stores both positive (target) and negative (non-target) feature information in a single weight vector, often obscuring fine-grained distinctions. Our approach, by contrast, maintains separate updates for target and non-target features, ultimately forming a single effective weight W = W₁ - W₂ that is more robust to noise and class imbalance. Experimental results on both regression (California Housing, Wine Quality) and classification (MNIST, Fashion-MNIST, CIFAR-10) tasks suggest that this decomposition enhances generalization and resists overfitting, especially when training data are sparse or noisy. Crucially, the inference complexity remains the same as in the standard WX + bias setup, offering a practical solution for improved learning without additional inference-time overhead.
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