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# Retrieval-Augmented Diffusion Models
Andreas Blattmann∗ Robin Rombach∗ Kaan Oktay Jonas Müller Björn Ommer LMU Munich, MCML & IWR, Heidelberg University, Germany
# Abstract
Novel architectures have recently improved generative image synthesis leading to excellent visual quality in various tasks. Much of this... | /Users/samarth/Documents/Samarth/CVPR/Nayana/pdfmathtranslate/miner/pdf/NeurIPS-2022-retrieval-augmented-diffusion-models-Paper-Conference.pdf | NeurIPS-2022-retrieval-augmented-diffusion-models-Paper-Conference_page_0 | [
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"$$\np_{\\theta,\\mathcal{D},\\xi_{k}}(x)=p_{\\theta}(x\\mid\\xi_{k}(x,\\mathcal{D}))=p_{\\theta}(x\\mid\\mathcal{M}_{\\mathcal{D}}^{(k)})\n$$",
"$$\np_{\\theta,\\mathcal{D},\\xi_{k}}(x)=p_{\\theta}(x\\mid\\{\\phi(y)\\mid y\\in\\xi_{k}(x,\\mathcal{D})\\}).\n$$",
"$$\n\\operatorname*{min}_{\\theta}\\mathcal{L}=\... | [
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"Figure 1: Our semi-parametric model outperforms the unconditional SOTA model ADM [15] on ImageNet [13] and even reaches the class-conditional ADM (ADM w/ classifier), while reducing parameter count. $|\\mathcal D|$ : Number of instances in database at inference; $|\\theta|$ : Number of tra... | [] | 0 | [
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# Retrieval-Augmented Diffusion Models
Andreas Blattmann∗ Robin Rombach∗ Kaan Oktay Jonas Müller Björn Ommer LMU Munich, MCML & IWR, Heidelberg University, Germany
# Abstract
Novel architectures have recently improved generative image synthesis leading to excellent visual quality in various tasks. Much of this... | /Users/samarth/Documents/Samarth/CVPR/Nayana/pdfmathtranslate/miner/pdf/NeurIPS-2022-retrieval-augmented-diffusion-models-Paper-Conference.pdf | NeurIPS-2022-retrieval-augmented-diffusion-models-Paper-Conference_page_1 | [
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"$$\np_{\\theta,\\mathcal{D},\\xi_{k}}(x)=p_{\\theta}(x\\mid\\xi_{k}(x,\\mathcal{D}))=p_{\\theta}(x\\mid\\mathcal{M}_{\\mathcal{D}}^{(k)})\n$$",
"$$\np_{\\theta,\\mathcal{D},\\xi_{k}}(x)=p_{\\theta}(x\\mid\\{\\phi(y)\\mid y\\in\\xi_{k}(x,\\mathcal{D})\\}).\n$$",
"$$\n\\operatorname*{min}_{\\theta}\\mathcal{L}=\... | [
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"Figure 1: Our semi-parametric model outperforms the unconditional SOTA model ADM [15] on ImageNet [13] and even reaches the class-conditional ADM (ADM w/ classifier), while reducing parameter count. $|\\mathcal D|$ : Number of instances in database at inference; $|\\theta|$ : Number of tra... | [] | 1 | [
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