Research map / Image generation / Fast diffusion sampling
Professor Forcing: A New Algorithm for Training Recurrent Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add adversarial discriminator matching free-running and teacher-forced hidden dynamics (Professor Forcing) | GRU language model · Penn Treebank char-level | validation BPC: 1.50 -> 1.48 | Helped |
| Professor Forcing | GRU language model · Penn Treebank word-level | perplexity | No clear effect |
| Professor Forcing | 3-layer GRU · Sequential MNIST | NLL | Helped |
| Professor Forcing | handwriting RNN · IAM-OnDB long sampling | human preference: 76.9% prefer PF (19.7+57.2) | Helped |
| Professor Forcing | raw waveform RNN · vocal synthesis | human rating: 1.30 -> 2.20 | Helped |
| Professor Forcing | raw waveform RNN · vocal synthesis, sequences shorter than 100 | NLL | No clear effect |
| Professor Forcing | RNN · vocal synthesis hidden states | train-vs-sampling hidden state distance: 22.8 -> 16.4 | Helped |
Closest papers by meaning
- Z-Forcing: Training Stochastic Recurrent Networks (NeurIPS 2017 · similarity 0.87)
- Twin Networks: Matching the Future for Sequence Generation (ICLR 2018 · similarity 0.85)
- Error Forcing in Recurrent Neural Networks (NeurIPS 2025 · similarity 0.85)
- When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective (NeurIPS 2025 · similarity 0.84)
- Adversarial Moment-Matching Distillation of Large Language Models (NeurIPS 2024 · similarity 0.83)
- Frequency Domain Reservoir Computing (NeurIPS 2026 · similarity 0.83)
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