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Scaling Autoregressive Video Models

ICLR 2020 · Video › Video generation · approach: Autoregressive long video generation · 14 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Scale the spatiotemporal subscaling model to large sizeVideo Transformer · BAIR Robot Pushingbits/dim: 1.53 -> 1.35Helped
Use spatiotemporal subscaling instead of spatial subscaling (ablation)Video Transformer · KineticsFVD: 263±6 -> 195±7Helped
Use single-frame generation instead of spatiotemporal subscaling (ablation)Video Transformer · Kineticsbits/dim: 1.19 -> 1.14Helped
Use spatiotemporal subscaling instead of single-frame generation (ablation)Video Transformer · KineticsFVD: 207±8 -> 170±5Helped
Use spatial subscaling rather than no subscaling (ablation)Video Transformer · Moving MNISTNats/Frame: 86.2 -> 91.8Hurt
Increase model size from base to largeVideo Transformer · KineticsFVD: 195±7 -> 170±5Helped
Increase hidden size (ablation)Video Transformer · BAIR Robot Pushing validationbits/dim: 1.65 -> 1.55 -> 1.47 -> 1.40Helped
Increase the number of layers (ablation)Video Transformer · BAIR Robot Pushing validationbits/dim: 1.63 -> 1.55 -> 1.48 -> 1.45Helped
Increase the number of attention heads (ablation)Video Transformer · BAIR Robot Pushing validationbits/dim: 1.59 -> 1.55 -> 1.51 -> 1.47Helped
Shuffle block-size ordering between layers (ablation)Video Transformer · BAIR Robot Pushing validationbits/dim: within 0.01 bits/dimNo clear effect
Lower sampling temperature from 1.0 to 0.9 (ablation)Video Transformer · BAIR Robot PushingFVD: 122±4Helped
Replace discretized one-hot input channels with continuous representations (ablation)Video Transformer · Video generationperformance and sample qualityHurt

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