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Evaluating Disentanglement of Structured Representations

ICLR 2022 · Detection and segmentation › Open-set and few-shot detection · approach: Object-centric learning theory and video · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add disentanglement regularization (ablation)GENESIS · Multi-dSpritesobject-level disentanglement (%): 64 ± 3 -> 90 ± 0.8Helped
add disentanglement regularization (ablation)IODINE · Multi-dSpritesobject-level disentanglement (%): 35 ± 2 -> 70 ± 9Helped
add disentanglement regularization (ablation)MONet (Att.) · Multi-dSpritesobject-level disentanglement (%): 60 ± 4 -> 52 ± 4Hurt
add disentanglement regularization (ablation)MONet (Dec.) · Multi-dSpritesobject-level disentanglement (%): 69 ± 0.3 -> 75 ± 7Helped
add disentanglement regularization (ablation)GENESIS · Multi-dSpritesARI (%): 96 ± 0.6 -> 85 ± 0Hurt
add disentanglement regularization (ablation)MONet (Att.) · Multi-dSpritesARI (%): 98 ± 0.1 -> 95 ± 1Hurt
add disentanglement regularization (ablation)MONet (Dec.) · Multi-dSpritesARI (%): 91 ± 0.2 -> 80 ± 4Hurt
add disentanglement regularization (ablation)GENESIS · CLEVR6object-level disentanglement (%): 43 ± 1 -> 50 ± 2Helped
add disentanglement regularization (ablation)MONet (Att.) · CLEVR6object-level disentanglement (%): 40 ± 9 -> 46 ± 5Helped
add disentanglement regularization (ablation)GENESIS · Multi-dSpritesproperty-level disentanglement (%): 65 ± 1 -> 72 ± 0.5Helped
use decoded masks (MONet Dec.) instead of attention masks (MONet Att.)MONet · Multi-dSpritesobject-level disentanglement (%): 52 ± 4 -> 75 ± 7Helped
use decoded masks (MONet Dec.) instead of attention masks (MONet Att.)MONet · Multi-dSpritesARI (%): 95 ± 1 -> 80 ± 4Hurt

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