MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation | Paper Citation Record

Observation 2cdb6f4d-7fe1-4e4e-84be-6bfd24ac2805 · outbound

This paper cites Denoising diffusion probabilistic models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Denoising diffusion probabilistic models,

Reference 1

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Observation b7046212-e4e6-4b92-b7fb-14493ddb4400 · outbound

This paper cites Diffusion models beat gans on image synthesis,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Diffusion models beat gans on image synthesis,

Reference 2

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Observation 8f82ce01-6dfb-4a73-8eb6-663154dd43be · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation High- resolution image synthesis with latent diffusion models,

Reference 3

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This paper cites Text- guided mask-free local image retouching,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Text- guided mask-free local image retouching,

Reference 4

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This paper cites Vidm: Video implicit diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Vidm: Video implicit diffusion models,

Reference 5

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Observation b32677a9-2474-40f8-a7f5-4b7deed740ab · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 6

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Observation 7295d146-953f-4fa3-9a01-0f2d2733fc0d · outbound

This paper cites Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Reference 7

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Observation c4e53dc8-6308-4bc1-88a9-1011889139d7 · outbound

This paper cites Diffusion-based layer-wise semantic reconstruction for unsupervised out-of-distribution detection,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Diffusion-based layer-wise semantic reconstruction for unsupervised out-of-distribution detection,

Reference 8

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Observation 11584a02-ea06-44c6-9d91-bb6adbacb464 · outbound

This paper cites Osdface: One-step diffusion model for face restoration,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Osdface: One-step diffusion model for face restoration,

Reference 9

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Observation bdb366e9-50f0-4711-99f6-962014b3a2e2 · outbound

This paper cites A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI

Reference 10

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Observation 5ffd6710-75d2-484e-8973-31fc363966ec · outbound

This paper cites Diffusion models in vision: A survey,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Diffusion models in vision: A survey,

Reference 11

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Observation b63a0f43-719a-46cf-94c6-e4a6faffe459 · outbound

This paper cites A resource-aware workload scheduling method for unbalanced gemms on gpus,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation A resource-aware workload scheduling method for unbalanced gemms on gpus,

Reference 12

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Observation a27867d3-068b-4de6-97fb-a312350390b1 · outbound

This paper cites Sketch-fusion: A gradient compression method with multi-layer fusion for communication- efficient distributed training,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Sketch-fusion: A gradient compression method with multi-layer fusion for communication- efficient distributed training,

Reference 13

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Observation 272b9f2e-f420-4ec5-8b8c-1c3a14e2784f · outbound

This paper cites Foundation models and intelligent decision-making: Progress, challenges, and perspectives,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Foundation models and intelligent decision-making: Progress, challenges, and perspectives,

Reference 14

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Observation d2ab61af-4706-425d-89f9-748a1f5fa789 · outbound

This paper cites A survey of quantization methods for efficient neural network infer- ence,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation A survey of quantization methods for efficient neural network infer- ence,

Reference 15

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Observation b9762ee9-bb06-4716-aeec-dade8746b094 · outbound

This paper cites Compression of convolutional neural networks: A short survey,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Compression of convolutional neural networks: A short survey,

Reference 16

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Observation bb64a371-e120-4030-a486-199824502db3 · outbound

This paper cites Reg-ptq: Regression- specialized post-training quantization for fully quantized object detec- tor,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Reg-ptq: Regression- specialized post-training quantization for fully quantized object detec- tor,

Reference 17

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Observation 285dad94-bf83-4d76-a444-c717abaab85e · outbound

This paper cites Scp: A structure combination pruning method via structured sparse for deep convolutional neural net- works,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Scp: A structure combination pruning method via structured sparse for deep convolutional neural net- works,

Reference 18

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Observation 7b0c8a48-0ec2-4bef-9c8b-426375d76fa4 · outbound

This paper cites A survey of techniques for optimizing transformer inference,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation A survey of techniques for optimizing transformer inference,

Reference 19

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This paper cites Q-mamba: Towards more efficient mamba models via post-training quantization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Q-mamba: Towards more efficient mamba models via post-training quantization,

Reference 20

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This paper cites QMamba: On First Exploration of Vision Mamba for Image Quality Assessment.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation QMamba: On First Exploration of Vision Mamba for Image Quality Assessment

Reference 21

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This paper cites Learned Step Size Quantization.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Learned Step Size Quantization

Reference 22

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Observation 9d634e07-11a1-4a77-aca4-5203781a9e58 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 23

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This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 24

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This paper cites BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models

Reference 25

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Observation 3d316bb4-871e-4f26-8ab7-78fdf7c332e6 · outbound

This paper cites Q-dm: An efficient low-bit quantized diffusion model,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Q-dm: An efficient low-bit quantized diffusion model,

Reference 26

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This paper cites BiDM: Pushing the Limit of Quantization for Diffusion Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation BiDM: Pushing the Limit of Quantization for Diffusion Models

Reference 27

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Observation 3b7ab945-efb3-4775-a249-7c125eaf362d · outbound

This paper cites Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming

Reference 28

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This paper cites Towards accurate post- training network quantization via bit-split and stitching,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Towards accurate post- training network quantization via bit-split and stitching,

Reference 29

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This paper cites QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 30

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This paper cites Pd-quant: Post-training quantization based on prediction difference metric,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Pd-quant: Post-training quantization based on prediction difference metric,

Reference 31

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This paper cites EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models

Reference 32

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Observation c2b79ab8-91f8-411c-a13c-1cf7648720d7 · outbound

This paper cites Mpq-dm: Mixed precision quantization for extremely low bit diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Mpq-dm: Mixed precision quantization for extremely low bit diffusion models,

Reference 33

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation LoRA: Low-Rank Adaptation of Large Language Models

Reference 34

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This paper cites Denoising Diffusion Implicit Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Denoising Diffusion Implicit Models

Reference 35

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This paper cites Scalable diffusion models with transformers,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Scalable diffusion models with transformers,

Reference 36

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Observation 1fcc8d90-5c6c-4769-8525-4784ce4b7edb · outbound

This paper cites Multi-party collaborative attention control for image customization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Multi-party collaborative attention control for image customization,

Reference 37

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Observation ed309121-fc1a-4670-a1d7-c0401e8f8c8c · outbound

This paper cites One-step effective diffusion network for real-world image super-resolution,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation One-step effective diffusion network for real-world image super-resolution,

Reference 38

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This paper cites Pseudo Numerical Methods for Diffusion Models on Manifolds.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Pseudo Numerical Methods for Diffusion Models on Manifolds

Reference 39

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Observation 85a251a3-eefb-4b11-8a97-fa3396604afd · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,

Reference 40

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Observation 1c3d29c2-c3fa-463f-aea2-c8d1f18e72c5 · outbound

This paper cites DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Reference 41

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Observation 7db33e1f-6c59-4c27-a3a9-71989d2e30f6 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Progressive Distillation for Fast Sampling of Diffusion Models

Reference 42

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This paper cites Consistency models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Consistency models,

Reference 43

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This paper cites Relational diffusion distillation for efficient image generation,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Relational diffusion distillation for efficient image generation,

Reference 44

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Observation 86e26931-433c-4b03-9323-d7a8c20173eb · outbound

This paper cites Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Q-VDiT: Towards Accurate Quantization and Distillation of Video-Generation Diffusion Transformers

Reference 45

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Observation eae78f77-1dca-454f-9ef1-1594d0536305 · outbound

This paper cites Diverse sample generation: Pushing the limit of generative data-free quantization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Diverse sample generation: Pushing the limit of generative data-free quantization,

Reference 46

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Observation 38268eb8-0a98-4881-801b-9a42e45ea8eb · outbound

This paper cites Pushing the limit of post-training quantization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Pushing the limit of post-training quantization,

Reference 47

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Observation d9900592-98d8-4397-b5f0-ce1d28bf5225 · outbound

This paper cites Hawq: Hessian aware quantization of neural networks with mixed-precision,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Hawq: Hessian aware quantization of neural networks with mixed-precision,

Reference 48

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Observation e55b3709-a2d3-478f-8f89-f166c643bb61 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Smoothquant: Accurate and efficient post-training quantization for large language models,

Reference 49

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This paper cites Q-diffusion: Quantizing diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Q-diffusion: Quantizing diffusion models,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:57:26.073085Z

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This paper cites Post-training quantiza- tion on diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Post-training quantiza- tion on diffusion models,

Reference 51

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This paper cites Ptqd: Accurate post-training quantization for diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Ptqd: Accurate post-training quantization for diffusion models,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:57:25.785627Z

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This paper cites Tfmq-dm: Temporal feature maintenance quantization for diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Tfmq-dm: Temporal feature maintenance quantization for diffusion models,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:57:25.570670Z

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This paper cites Towards accu- rate post-training quantization for diffusion models,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Towards accu- rate post-training quantization for diffusion models,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:57:25.356543Z

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Observation 00657131-b141-4fbc-914a-05c4c4633a78 · outbound

This paper cites PTQ4DiT: Post-training Quantization for Diffusion Transformers.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation PTQ4DiT: Post-training Quantization for Diffusion Transformers

Reference 55

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This paper cites QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning

Reference 56

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Observation e96810b9-489a-4852-95a0-b5d3ec9ebcbf · outbound

This paper cites Ompq: Orthogonal mixed precision quantization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Ompq: Orthogonal mixed precision quantization,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:57:25.202272Z

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Observation 9722269a-dc1a-4947-8f8a-7be18db64b33 · outbound

This paper cites dabnn: A super fast inference framework for binary neural networks on arm devices,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation dabnn: A super fast inference framework for binary neural networks on arm devices,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:57:25.025696Z

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Observation b9935fa1-1505-4895-8dfb-7029b00b1d86 · outbound

This paper cites Online knowledge distillation via mutual contrastive learning for visual recogni- tion,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Online knowledge distillation via mutual contrastive learning for visual recogni- tion,

Reference 59

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Observation c20b336f-27a3-4c83-aa91-95ae95dda740 · outbound

This paper cites Self-supervised visual feature learning with deep neural networks: A survey,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Self-supervised visual feature learning with deep neural networks: A survey,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:57:24.863227Z

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Observation 47fff58d-d3ff-4b8c-ac1a-1cc33bcaaade · outbound

This paper cites A generalization of the eckart-young-mirsky matrix approximation theorem,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation A generalization of the eckart-young-mirsky matrix approximation theorem,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:57:24.689939Z

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Observation 7903f5e3-6f8b-4b4a-8e50-c7926ace7a8a · outbound

This paper cites Hawq-v3: Dyadic neural net- work quantization,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Hawq-v3: Dyadic neural net- work quantization,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:57:24.552525Z

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Observation b020707e-8694-4dc9-b7b3-2c5ec2182059 · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 63

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Observation c1b544f2-9055-4e54-b68f-5c9cc3f9dc05 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Imagenet: A large-scale hierarchical image database,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T19:57:23.121768Z

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Observation e836c862-bcf7-4ba1-a6c4-677017c45b6a · outbound

This paper cites Microsoft coco: Common objects in context,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Microsoft coco: Common objects in context,

Reference 65

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Observation 3c4bde06-95ab-4039-8a5b-c7e640449cb4 · outbound

This paper cites Improved techniques for training gans,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Improved techniques for training gans,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T19:57:23.293982Z

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Observation db416036-5d8c-48dc-929a-7813f8f02415 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T19:57:23.396871Z

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Observation 0e01c73a-7991-4632-beb1-d78244652435 · outbound

This paper cites Generating Images with Sparse Representations.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Generating Images with Sparse Representations

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T19:57:23.528462Z

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Observation ca152dbc-485e-4baf-985b-42bddba045af · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T19:57:23.634113Z

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Observation 9babebd8-f55a-46e4-a5fc-46935ec5c5de · outbound

This paper cites Cross- image relational knowledge distillation for semantic segmentation,.

MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation Cross- image relational knowledge distillation for semantic segmentation,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T19:57:23.734370Z

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