HyperAI

Video Prediction On Kth

المقاييس

Cond
FVD
LPIPS
PSNR
Params (M)
Pred
SSIM
Train

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

جدول المقارنة
اسم النموذجCondFVDLPIPSPSNRParams (M)PredSSIMTrain
stochastic-video-generation-with-a-learned10157.90.12923.9122.8400.80010
stochastic-adversarial-video-prediction10--27.77-200.852-
z-order-recurrent-neural-networks-for-video10--27.58-200.817-
decomposing-motion-and-content-for-natural10--26.29-200.806-
convolutional-tensor-train-lstm-for-spatio10-0.19627.62-200.815-
diverse-video-generation-using-a-gaussian-1--------
video-prediction-recalling-long-term-motion10-159.827.5-400.879-
slamp-stochastic-latent-appearance-and-motion10228 ± 50.0795±0.003429.39±0.30-300.8646±0.005010
stochastic-variational-video-prediction10253.50.26025.708.3400.77210
predrnn-a-recurrent-neural-network-for10-0.13928.37-200.839-
stochastic-latent-residual-video-prediction-110222 ± 30.0736±0.002929.69±032-300.8697±0.004610
unsupervised-learning-of-object-structure-and10395.00.12424.292.3400.76610
convolutional-lstm-network-a-machine-learning10-0.23123.58-200.712-
msnet-mutual-suppression-network-for10--27.08-200.876-
stochastic-variational-video-prediction10209.50.23225.878.3400.78210
stochastic-adversarial-video-prediction10145.70.11626.007.3400.80610
varnet-exploring-variations-for-unsupervised10--28.48-200.843-
dynamic-filter-networks10--27.26-200.794-
decomposing-motion-and-content-for-natural10--25.95-200.804-
accurate-grid-keypoint-learning-for-efficient10144.20.09227.112.0400.83710
video-pixel-networks10--23.76-200.746-
stochastic-adversarial-video-prediction10183.70.12623.7917.6400.69910
deep-learning-for-precipitation-nowcasting-a10--26.97-200.790-
folded-recurrent-neural-networks-for-future10--26.12-200.771-
eidetic-3d-lstm-a-model-for-video-prediction10--29.31-200.879-
stochastic-video-generation-with-a-learned10377 ± 60.0923±0.003828.06±0.29-300.8438±0.005410
predrnn-towards-a-resolution-of-the-deep-in10--28.47-200.865-
stochastic-adversarial-video-prediction10374 ± 30.1120±0.003926.51±0.29-300.7564±0.006210
exploring-spatial-temporal-multi-frequency10--29.85-200.893-
stochastic-variational-video-prediction10636 ± 10.2049±0.005328.19±0.31-300.83810
video-prediction-at-multiple-scales-with--0.02927.81--0.951-