Title: Kuromi: Learning without Augmentation via Energy-based Semi-supervised Kuramoto Neurons
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Semi-supervised Learning

Abstract: Semi-supervised learning (SSL) often relies on extensive data augmentation or complex teacher-student structures to generate reliable supervision from unlabeled data. In this work, we propose \textbf{Kuromi}, a novel SSL framework that discards augmentation entirely and instead leverages energy-based dynamics for representation learning and pseudo-label generation. Central to Kuromi is the \textit{Artificial Kuramoto Oscillatory Neuron} (AKOrN), a biologically inspired dynamic neuron model that encodes inputs as synchronized oscillatory states on a hypersphere. Through unsupervised pretraining, fine-tuning on limited labels, and energy-guided self-training, Kuromi produces low-energy, structure-aware feature representations without requiring external regularization. To better exploit labeled data and pseudo-labels, we propose \textbf{Energize}, an iterative, augmentation-free latent-state smoothing method inspired by\textit{ Weisfeiler-Lehman} aggregation, which operates entirely in the prediction space without hyperparameters. Extensive experiments on CIFAR-10, SVHN, STL-10, and ImageNet show that Kuromi achieves state-of-the-art performance among non-transformer SSL methods and remains competitive with recent transformer-based approaches. Notably, Kuromi not only achieves state-of-the-art results on CIFAR-10 and STL-10, but also leads in efficiency with just 12M parameters, 0.18 GFLOPs, and the highest throughput across all baselines.

TL;DR: A semi-supervised learning framework without data augmentation