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This research introduces "ProtoSeam," a training technique that inserts learnable class prototypes at a network's semantic interface to boost classification accuracy without altering the inference architecture. By combining quadratic consensus penalties with standard loss functions, the method achieves up to five percentage points improvement on benchmarks like CIFAR-10 and TinyImageNet while maintaining theoretical rigor. The approach offers a practical pathway for enhancing model performance through architectural agnostic training modifications rather than hardware or algorithmic overhauls.
ProtoSeam: Lifting Classifier Training with Latent Gaussian Mixture Models
arXiv:2609.35174v1 Announce Type: new Abstract: We propose a lifted reformulation of supervised classification that improves the final accuracy of standard classifiers without changing the architecture at inference time. A network $N=N_2\circ N_1$ is split at a single semantic interface and one learnable prototype per class is inserted there. Training combines a quadratic consensus penalty that pulls $N_1(x)$ toward the prototype of its class with a classification loss of $N_2$ evaluated on samples drawn around the prototypes, whereat no gradient crosses the interface. At inference the prototypes are discarded and the unmodified network $N_2\circ N_1$ is used. Across CIFAR-10, CIFAR-100, and TinyImageNet with ResNet and vision transformer backbones, lifted training improves test accuracy by up to five percentage points over variants without lifting under a shared tuning protocol. Moreover, we provide theoretical justification of those results.
Abstract:We propose a lifted reformulation of supervised classification that improves the final accuracy of standard classifiers without changing the architecture at inference time. A network $N=N_2\circ N_1$ is split at a single semantic interface and one learnable prototype per class is inserted there. Training combines a quadratic consensus penalty that pulls $N_1(x)$ toward the prototype of its class with a classification loss of $N_2$ evaluated on samples drawn around the prototypes, whereat no gradient crosses the interface. At inference the prototypes are discarded and the unmodified network $N_2\circ N_1$ is used. Across CIFAR-10, CIFAR-100, and TinyImageNet with ResNet and vision transformer backbones, lifted training improves test accuracy by up to five percentage points over variants without lifting under a shared tuning protocol. Moreover, we provide theoretical justification of those results.
Machine Learning (cs.LG)
arXiv:2609.35174 [cs.LG]
(or arXiv:2609.35174v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.35174 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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