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3D Architecture

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

https://github.com/sentient-engineering/agent-q

Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems

https://github.com/ser-art/RAE-vs-AE

Relational Autoencoder for Feature Extraction

https://github.com/setsunil/dsdnet

Improving Image Restoration by Revisiting Global Information Aggregation

https://github.com/sevenysw/python_segy

Deep learning for denoising

https://github.com/shangqigao/RONet

Rank-One Network: An Effective Framework for Image Restoration

https://github.com/shaonianruntu/MWCNN

Multi-level Wavelet-CNN for Image Restoration

https://github.com/sharonwang1/seg_denoising

Segmentation-Aware Image Denoising without Knowing True Segmentation

https://github.com/shibuiwilliam/DeepLearningDenoise

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

https://github.com/shihaozhaozsh/lavi-bridge

ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

https://github.com/shijx12/kqapro_baselines

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/shilin-lu/mace

Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts

https://github.com/shim0114/T2V-Diffusion-Search

Inference-Time Text-to-Video Alignment with Diffusion Latent Beam Search

https://github.com/shinechen1024/magicclothing

OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on

https://github.com/shivamsaboo17/Deep-Restore-PyTorch

Noise2Noise: Learning Image Restoration without Clean Data

https://github.com/shmsw25/bart-closed-book-qa

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/showlab/FAR

MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation

https://github.com/showlab/Tune-A-Video

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/showlab/loveu-tgve-2023

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/shreya2509/Scheduled-Denoising-Autoencoder

Scheduled denoising autoencoders

https://github.com/shuaiyihuang/scorrsan

Learning Semantic Correspondence with Sparse Annotations

https://github.com/shuhongchen/panic3d-anime-reconstruction

PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters

https://github.com/siavashBigdeli/DDE

Learning Generative Models using Denoising Density Estimators

https://github.com/siavashbigdeli/DMSP

Deep Mean-Shift Priors for Image Restoration

https://github.com/sillsill777/Diffusion

Denoising Diffusion Probabilistic Models

https://github.com/sisinflab/formal-multimod-rec

A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation

https://github.com/sixuli/ddpm_and_kde

On Memorization in Diffusion Models

https://github.com/sjames40/adversarial-purification-for-mri

Score-based diffusion models for accelerated MRI

https://github.com/sjmoran/difar

CURL: Neural Curve Layers for Global Image Enhancement

https://github.com/sjmoran/neural_curve_layers

CURL: Neural Curve Layers for Global Image Enhancement

https://github.com/skchen1993/SwinIR

SwinIR: Image Restoration Using Swin Transformer

https://github.com/skt-ai/kobart

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/skyworkai/skyreels-a2

SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers

https://github.com/skyworkai/skyreels-v2

SkyReels-Audio: Omni Audio-Conditioned Talking Portraits in Video Diffusion Transformers

https://github.com/sldyns/DnCNN_paddle

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

https://github.com/sldyns/MIRNet_paddle

Learning Enriched Features for Real Image Restoration and Enhancement

https://github.com/softsys4ai/FlexiBO

ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense

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