  Recent works have studied implicit biases in deep learning, especially the
behavior of last-layer features and classifier weights. However, they usually
need to simplify the intermediate dynamics under gradient flow or gradient
descent due to the intractability of loss functions and model architectures. In
this paper, we introduce the unhinged loss, a concise loss function, that
offers more mathematical opportunities to analyze the closed-form dynamics
while requiring as few simplifications or assumptions as possible. The unhinged
loss allows for considering more practical techniques, such as time-vary
learning rates and feature normalization. Based on the layer-peeled model that
views last-layer features as free optimization variables, we conduct a thorough
analysis in the unconstrained, regularized, and spherical constrained cases, as
well as the case where the neural tangent kernel remains invariant. To bridge
the performance of the unhinged loss to that of Cross-Entropy (CE), we
investigate the scenario of fixing classifier weights with a specific
structure, (e.g., a simplex equiangular tight frame). Our analysis shows that
these dynamics converge exponentially fast to a solution depending on the
initialization of features and classifier weights. These theoretical results
not only offer valuable insights, including explicit feature regularization and
rescaled learning rates for enhancing practical training with the unhinged
loss, but also extend their applicability to other loss functions. Finally, we
empirically demonstrate these theoretical results and insights through
extensive experiments.
