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PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add zigzag spatiotemporal memory flow (ablation) | ConvLSTM (128 × 4) · Moving MNIST-2 | MSE/frame: 103.3 -> 74.0 | Helped |
| replace ConvLSTM with ST-LSTM units (ablation) | PredRNN with spatiotemporal memory M · Moving MNIST-2 | MSE/frame: 74.0 -> 56.8 | Helped |
| replace ConvLSTM with ST-LSTM units (ablation) | PredRNN with spatiotemporal memory M · Moving MNIST-3 | MSE/frame: 118.2 -> 93.4 | Helped |
| PredRNN instead of VPN baseline | video prediction · Radar echo | MSE/frame: 60.7 -> 44.2 | Helped |
| PredRNN instead of VPN baseline | video prediction · Radar echo | memory usage: 11513 MB -> 2367 MB | Helped |
| spatiotemporal memory only vs VPN baseline (ablation) | PredRNN · Moving MNIST-2 | CE/frame: 110.1 vs 118.5 | No clear effect |
Closest papers by meaning
- PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning (ICML 2018 · similarity 0.92)
- Flexible Spatio-Temporal Networks for Video Prediction (CVPR 2017 · similarity 0.88)
- Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning (CVPR 2023 · similarity 0.88)
- Unsupervised Transfer Learning for Spatiotemporal Predictive Networks (ICML 2020 · similarity 0.88)
- Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics (CVPR 2019 · similarity 0.88)
- Wavelet-Driven Spatiotemporal Predictive Learning: Bridging Frequency and Time Variations (AAAI 2024 · similarity 0.87)
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