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Active Image Indexing

ICLR 2023 · Image generation › Autoregressive and latent generation · approach: Generalizable fake image detection · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Activate indexed images by optimizing image pixels toward their index quantizationIVF-PQ, SSCD ResNet50 · DISC21, Ref10k transformed queries (Avg.)Recall@1: 0.45 -> 0.75Helped
Activate indexed imagesIVF 1024 · DISC21, Query50k transformed queries (Avg.)Recall@1: 0.47 -> 0.83Helped
Activate indexed imagesIVF 1024 · DISC21, Query50k transformed queriesμAP: 0.16 -> 0.43Helped
Activate indexed imagesOPQ 8x8 · DISC21, Query50k transformed queriesμAP: 0.48 -> 0.55Helped
Activate indexed imagesPCA64 LSH · DISC21, Query50k transformed queriesμAP: 0.25 -> 0.39Helped
Use 1 optimization step at learning rate 10 instead of 10 steps at learning rate 1 (ablation)Active indexing with IVF-PQ · DISC21, Ref10k transformed queries (Avg.)Recall@1: 0.75 -> 0.72Hurt
Use 1 optimization step at learning rate 10 instead of 10 steps at learning rate 1 (ablation)Active indexing with IVF-PQ · DISC21, identity queriesRecall@1: 1.00 -> 0.99Hurt
Use expectation over transformations during activation (ablation)Active indexing with IVF-PQ · DISC21, Ref10k transformed queries (Avg.)Recall@1: 0.75 -> 0.76Helped
Use expectation over transformations during activation (ablation)Active indexing with IVF-PQ · DISC21, rotation 90 queriesRecall@1: 0.50 -> 0.64Helped

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