Deep studying fashions excel in stationary information however wrestle in non-stationary environments resulting from a phenomenon often called lack of plasticity (LoP), the degradation of their capability to study sooner or later. This work presents a first-principles investigation of LoP in gradient-based studying. Grounded in dynamical methods concept, we formally outline LoP by figuring out steady manifolds within the parameter house that lure gradient trajectories. Our evaluation reveals two major mechanisms that create these traps: frozen models from activation saturation and cloned-unit manifolds from representational redundancy. Our framework uncovers a basic stress: properties that promote generalization in static settings, reminiscent of low-rank representations and ease biases, straight contribute to LoP in continuous studying situations. We validate our theoretical evaluation with numerical simulations and discover architectural selections or focused perturbations as potential mitigation methods.
