# Progressive Neural Network

Progressive neural networks transfer knowledge by keeping every frozen earlier column, so stored state and inference grow with each generation. WeanNet keeps a single frozen parent, feeds its layer-wise activations to a fresh student through a scalar gain, and decays that gain linearly to zero during training, so the parent can be removed at deployment. On a concept-drift task it found the new correct action on drifted states more often than the architecturally matched PNN-Reset control at every tested entropy setting, though it also led on never-drifted states and stayed comparable to training from scratch.

Progressive neural networks transfer knowledge by keeping every frozen earlier column, so stored state and inference grow with each generation. WeanNet keeps a single frozen parent, feeds its layer-wise activations to a fresh student through a scalar gain, and decays that gain linearly to zero during training, so the parent can be removed at deployment. On a concept-drift task it found the new correct action on drifted states more often than the architecturally matched PNN-Reset control at every tested entropy setting, though it also led on never-drifted states and stayed comparable to training from scratch.