https://github.com/lamda-bbo/offline-moo
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 6
- 보존된 평가 기록
- 15
Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.
<span style="color:grey; opacity: 0.6">( Image credit: Beyond a Gaussian Denoiser )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/laomao0/BIN
https://github.com/lavolpiana/deep-image-prior
https://github.com/layer6ai-labs/rectangular-flows
Flows for simultaneous manifold learning and density estimation
https://github.com/layer6ai-labs/tr0n
clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIP
https://github.com/lee-aaron/Image-Super-Resolution
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/leekanggeun/ISCL
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image Denoising
https://github.com/leffff/FlowModels
Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment
https://github.com/leftthomas/restormer
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/leili666/denoise_deep_learning
https://github.com/leonmakise/NBNet_Pytorch
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
https://github.com/liaopeiyuan/pndm
https://github.com/lightning-uq-box/lightning-uq-box
https://github.com/lilijiangg/autodiffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/lim0606/pytorch-ardae-rl
https://github.com/lim0606/pytorch-ardae-vae
https://github.com/lime-j/nafnet-jax
https://github.com/lindonroberts/inexact_dfo_bilevel_learning
https://github.com/linxin0/low_light_rainy
https://github.com/litalby1/make-it-count
Make It Count: Text-to-Image Generation with an Accurate Number of Objects
https://github.com/ljl5261/mmm-rs
https://github.com/ljw9510/SDL
https://github.com/ljw9510/smf
https://github.com/lllyasviel/fooocus
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
https://github.com/lllyasviel/omost
Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following
https://github.com/lmnt-com/diffwave
https://github.com/lmnt-com/wavegrad
https://github.com/logchan/dde
Learning Generative Models using Denoising Density Estimators
https://github.com/lonnyzhang423/github-hot-hub
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/lorenzo-stacchio/Stable-Diffusion-Inpaint
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/lscientific/dhlcm
Heteroskedastic PCA: Algorithm, Optimality, and Applications
https://github.com/lshaw8317/mlmcfordms
https://github.com/lucastheis/isa
https://github.com/lucidrains/DALLE2-pytorch
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
https://github.com/lucidrains/ddpm-proteins
https://github.com/lucidrains/denoising-diffusion-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.