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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/divyakraman/imposterdiffusion2024

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ImPoster: Text and Frequency Guidance for Subject Driven Action Personalization using Diffusion Models

https://github.com/dlr-mi/atme

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Look ATME: The Discriminator Mean Entropy Needs Attention

https://github.com/dome272/paella

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A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces

https://github.com/donahowe/theatergen

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TheaterGen: Character Management with LLM for Consistent Multi-turn Image Generation

https://github.com/dongjin-seo2020/adjointdiffusion

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Physics-guided and fabrication-aware inverse design of photonic devices using diffusion models

https://github.com/dongqi-fan/irconstyle

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IRConStyle: Image Restoration Framework Using Contrastive Learning and Style Transfer

https://github.com/dongqi-fan/lir

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LIR: A Lightweight Baseline for Image Restoration

https://github.com/dongrunmin/refdiff

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Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model

https://github.com/dongshengze/scrdit

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scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling

https://github.com/dongzhuoyao/diffusion-representation-learning-survey-taxonomy

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Diffusion Models and Representation Learning: A Survey

https://github.com/donydchen/mvsplat360

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MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse Views

https://github.com/dreadbird06/tso_vace_wpe

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Task-specific Optimization of Virtual Channel Linear Prediction-based Speech Dereverberation Front-End for Far-Field Speaker Verification

https://github.com/dreamyang-liu/SciAnnotate

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SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling

https://github.com/drorsimon/CSCNet

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Rethinking the CSC Model for Natural Images

https://github.com/ds2p/crsae

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Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning

https://github.com/ds2p/dea

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Convolutional dictionary learning based auto-encoders for natural exponential-family distributions

https://github.com/dtc111111/neslam

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NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

https://github.com/dualparal-project/dualparal

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Minute-Long Videos with Dual Parallelisms

https://github.com/duanhuiyu/maeip_csformer

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Masked Autoencoders as Image Processors

https://github.com/duanlab123/curvpnp

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CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoiser

https://github.com/duanlab123/mgmc

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Fast Multi-grid Methods for Minimizing Curvature Energy

https://github.com/duanyiqun/diffusiondepth

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DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation

https://github.com/dujiazhi/CFMNet

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Flexible Image Denoising with Multi-layer Conditional Feature Modulation

https://github.com/durgas16/AMAL

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Adaptive Mixing of Auxiliary Losses in Supervised Learning

https://github.com/duyguceylan/pix2video

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Pix2Video: Video Editing using Image Diffusion

https://github.com/dvlab-research/Simple-SR

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LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond

https://github.com/dvlab-research/jenga

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Training-Free Efficient Video Generation via Dynamic Token Carving

https://github.com/dynamicslab/modified-SINDy

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Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Data

https://github.com/dzh19990407/lbdt

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Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation

https://github.com/dzungdoan6/tc-pdm

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TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video Translation

https://github.com/eai-lab/on-device-sora

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On-device Sora: Enabling Training-Free Diffusion-based Text-to-Video Generation for Mobile Devices

https://github.com/ebalti/Double-Generalized-Gamma-Distribution/blob/main/README.md

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Tractable Approach to MmWaves Cellular Analysis with FSO Backhauling under Feedback Delay and Hardware Limitations

https://github.com/echonet/image_quality

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EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise

https://github.com/edibert4imagemanipulation/edibert

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EdiBERT, a generative model for image editing

https://github.com/edunlp/nemo-multistage-classroom-diarization

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Multi-Stage Speaker Diarization for Noisy Classrooms

https://github.com/edward-sun/difusco

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DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.