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Monotonic linear interpolation (MLI) - on the line connecting a randominitialization with the minimizer it converges to, the loss and accuracy aremonotonic - is a phenomenon that is commonly observed in the training of neuralnetworks. Such a phenomenon may seem to suggest that optimization of neuralnetworks is easy. In this paper, we show that the MLI property is notnecessarily related to the hardness of optimization problems, and empiricalobservations on MLI for deep neural networks depend heavily on biases. Inparticular, we show that interpolating both weights and biases linearly leadsto very different influences on the final output, and when different classeshave different last-layer biases on a deep network, there will be a longplateau in both the loss and accuracy interpolation (which existing theory ofMLI cannot explain). We also show how the last-layer biases for differentclasses can be different even on a perfectly balanced dataset using a simplemodel. Empirically we demonstrate that similar intuitions hold on practicalnetworks and realistic datasets.

Plateau in Monotonic Linear Interpolation -- A "Biased" View of Loss Landscape for Deep Networks