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PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace ST-LSTMs with causal LSTMs (ablation) | PredRNN · MNIST-2, 10 time steps | SSIM: 0.867 -> 0.882 | Helped |
| swap the spatial and temporal memory update order (ablation) | Causal LSTM · MNIST-2, 10 time steps | SSIM: 0.882 -> 0.875 | Hurt |
| add a GHU (ablation) | PredRNN · MNIST-2, 10 time steps | SSIM: 0.867 -> 0.886 | Helped |
| add a GHU to Causal LSTM (ablation) | Causal LSTM · MNIST-2, 10 time steps | SSIM: 0.882 -> 0.898 | Helped |
| place the GHU between causal LSTM layers 1 and 2 rather than layers 2 and 3 (ablation) | Causal LSTM + GHU · MNIST-2, 10 time steps | SSIM: 0.894 -> 0.898 | Helped |
| place the GHU between causal LSTM layers 1 and 2 rather than layers 3 and 4 (ablation) | Causal LSTM + GHU · MNIST-2, 10 time steps | SSIM: 0.885 -> 0.898 | Helped |
| use PredRNN++ with causal LSTMs and a GHU | PredRNN · KTH test set, 20 predicted frames | PSNR: 27.55 -> 28.47 | Helped |
Closest papers by meaning
- PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs (NeurIPS 2017 · similarity 0.92)
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- TTVidT: Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining (NeurIPS 2026 · similarity 0.84)
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