Video Prediction On Kth

المقاييس

Cond

FVD

LPIPS

PSNR

Params (M)

Pred

SSIM

Train

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	Cond	FVD	LPIPS	PSNR	Params (M)	Pred	SSIM	Train	Paper Title	Repository
SVG-LP (from Grid-keypoints)	10	157.9	0.129	23.91	22.8	40	0.800	10	Stochastic Video Generation with a Learned Prior
SAVP-VAE	10	-	-	27.77	-	20	0.852	-	Stochastic Adversarial Video Prediction
Znet	10	-	-	27.58	-	20	0.817	-	Z-Order Recurrent Neural Networks for Video Prediction	-
MCnet + Residual	10	-	-	26.29	-	20	0.806	-	Decomposing Motion and Content for Natural Video Sequence Prediction
Conv-TT-LSTM	10	-	0.196	27.62	-	20	0.815	-	Convolutional Tensor-Train LSTM for Spatio-temporal Learning
DVG	-	-	-	-	-	-	-	-	Diverse Video Generation using a Gaussian Process Trigger
LMC	10	-	159.8	27.5	-	40	0.879	-	Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning
SLAMP	10	228 ± 5	0.0795±0.0034	29.39±0.30	-	30	0.8646±0.0050	10	SLAMP: Stochastic Latent Appearance and Motion Prediction
SV2P time-invariant (from Grid-keypoints)	10	253.5	0.260	25.70	8.3	40	0.772	10	Stochastic Variational Video Prediction
PredRNN-V2	10	-	0.139	28.37	-	20	0.839	-	PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
SRVP	10	222 ± 3	0.0736±0.0029	29.69±032	-	30	0.8697±0.0046	10	Stochastic Latent Residual Video Prediction
Struct-VRNN (from Grid-keypoints)	10	395.0	0.124	24.29	2.3	40	0.766	10	Unsupervised Learning of Object Structure and Dynamics from Videos
ConvLSTM	10	-	0.231	23.58	-	20	0.712	-	Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
MSNET	10	-	-	27.08	-	20	0.876	-	Mutual Suppression Network for Video Prediction using Disentangled Features	-
SV2P time-invariant (from Grid-keypoints)	10	209.5	0.232	25.87	8.3	40	0.782	10	Stochastic Variational Video Prediction
SAVP-VAE (from Grid-keypoints)	10	145.7	0.116	26.00	7.3	40	0.806	10	Stochastic Adversarial Video Prediction
VarNet	10	-	-	28.48	-	20	0.843	-	VarNet: Exploring Variations for Unsupervised Video Prediction
DFN	10	-	-	27.26	-	20	0.794	-	Dynamic Filter Networks
MCnet	10	-	-	25.95	-	20	0.804	-	Decomposing Motion and Content for Natural Video Sequence Prediction
Grid-keypoints	10	144.2	0.092	27.11	2.0	40	0.837	10	Accurate Grid Keypoint Learning for Efficient Video Prediction

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