Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction

arXiv:2605.09697 · cs.CV, cs.LG · Submitted 2026-05-10 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-05-10

Updated: 2026-09-10

Comments: 7 pages, 1 figure

Code: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

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

Terminology

• Discriminative Span — 35× in this paper
• Alignment — 31× in this paper · explained in 1 episode
• Generalization — 23× in this paper · explained in 13 episodes
• Embeddings — 16× in this paper · explained in 2 episodes
• Linear Probing — 11× in this paper · explained in 2 episodes
• Cosine Similarity — 9× in this paper · explained in 3 episodes
• Foundation Model — 9× in this paper · explained in 3 episodes
• F1 Score — 8× in this paper · explained in 3 episodes
• Fidelity — 8× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Regularization — 6× in this paper · explained in 3 episodes
• Synthetic Data Generation — 6× in this paper · explained in 5 episodes
• Data Augmentation — 4× in this paper · explained in 7 episodes
• Foundation Models — 4× in this paper · explained in 7 episodes
• Latent Space — 4× in this paper · explained in 7 episodes
• Transferability — 4× in this paper · explained in 4 episodes
• Core — 3× in this paper · explained in 2 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Singular Value Decomposition — 3× in this paper
• Singular Value Decomposition (SVD) — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Non-Negative Least Squares — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment Problem — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Inception Score — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Mode Collapse — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Pneumonia Chest X-ray — 1× in this paper
• Redundancy — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

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