Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
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We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with L layers of minimal widths r_1^*, …, r_L-1^* reaches a zero-loss minimum at r_1^*! ⋯ r_L-1^*! isolated points that are permutations of one another, we show that adding one extra neuron to each layer is sufficient to connect all these previously discrete minima into a single manifold. For a two-layer overparameterized network of width r^*+ h =: m we explicitly describe the manifold of global minima: it consists of T(r^*, m) affine subspaces of dimension at least h that are connected to one another. For a network of width m, we identify the number G(r,m) of affine subspaces containing only symmetry-induced critical points that are related to the critical points of a smaller network of width r<r^*. Via a combinatorial analysis, we derive closed-form formulas for T and G and show that the number of symmetry-induced critical subspaces dominates the number of affine subspaces forming the global minima manifold in the mildly overparameterized regime (small h) and vice versa in the vastly overparameterized regime (h ≫ r^*). Our results provide new insights into the minimization of the non-convex loss function of overparameterized neural networks.
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