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Your head is there to move you around: Goal-driven models of the primate dorsal pathway

NeurIPS 2021 · Label-efficient and robust learning › Self-supervised and contrastive · approach: Self-supervised neuroimaging representations · 54 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
train 3D ResNet to predict self-motion on AirSim instead of action recognition on Kinetics4003D ResNet · MST (packlab-mst)normalized R: .224 -> .454Helped
self-motion objective instead of action recognition3D ResNet · V1 (pvc1)normalized R: .471 -> .364Hurt
train CPC on AirSim self-motion videos instead of UCF101 (ablation)CPC network · MST (packlab-mst)normalized R: .190 -> .292Helped
train CPC on AirSim instead of UCF101 (ablation)CPC network · MT (mt2)normalized R: .241 -> .360Helped
supervised self-motion objective instead of CPC on AirSim (ablation)AirSim-trained network · MST (packlab-mst)normalized R: .292 -> .454Helped
add motion energy (gabor with motion vs gabor_nomotion) (ablation)Gabor pyramid · MST (packlab-mst)normalized R: .248 -> .394Helped
change input scale to 0.66X or 1.5X (ablation)DorsalNet layer mapping for V1 · best matching layermean layer assignment: 1.0 at 0.66X, 1.1 at 1X, 1.3 at 1.5XNo clear effect

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