Follow the Geometry, Not the Model: Cold Start Semi-Supervised Learning

arXiv:2609.14451 · cs.LG, cs.AI · Submitted 2026-09-13 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-13

Updated: 2026-09-13

Code: https://github.com/microsoft/Semi-supervised-learning

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 40× in this paper · explained in 1 episode
• Distillation — 40× in this paper · explained in 2 episodes
• Embeddings — 38× in this paper · explained in 2 episodes
• Semi-supervised Learning — 20× in this paper · explained in 4 episodes
• Pseudo-labels — 15× in this paper · explained in 2 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Consistency — 8× in this paper · explained in 3 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Graph Construction — 4× in this paper · explained in 3 episodes
• Label Propagation — 4× in this paper
• Pseudo-labeling — 4× in this paper · explained in 3 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Core — 3× in this paper · explained in 2 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• In International Conference on Machine Learning — 3× in this paper
• Semi-Supervised Learning (SSL) — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• Self-Supervised Learning — 2× in this paper · explained in 8 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Calibration Failure — 1× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Spectral clustering — 1× in this paper · explained in 2 episodes
• Veracity-Aware Semi-Supervised Training — 1× in this paper
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples

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