https://github.com/ubc-vision/lsenerf
공식 코드LSE-NeRF: Learning Sensor Modeling Errors for Deblured Neural Radiance Fields with RGB-Event Stereo
Papers with Code 아카이브
Image Generation
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ubcdingxin/ccdm
공식 코드CCDM: Continuous Conditional Diffusion Models for Image Generation
https://github.com/ucdvision/genie
공식 코드https://github.com/uchidalab/docker-montage-gan
공식 코드MontageGAN: Generation and Assembly of Multiple Components by GANs
https://github.com/ucla-starai/tiramisu
공식 코드https://github.com/uclaml/dndm
공식 코드Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time
https://github.com/uclanlp/diverse-factual
공식 코드https://github.com/ucsb-nlp-chang/copaint
공식 코드Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models
https://github.com/ucsb-nlp-chang/diffusion-spacetime-attn
공식 코드https://github.com/ucsb-nlp-chang/diffusion_resampling
공식 코드https://github.com/uhlerlab/mol2image
공식 코드Improved Conditional Flow Models for Molecule to Image Synthesis
https://github.com/uhlerlab/ntk_matrix_completion
공식 코드Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks
https://github.com/umar07/Image_Forgery_Detection
공식 코드Analysing Statistical methods for Automatic Detection of Image Forgery
https://github.com/umass-embodied-agi/mirage
공식 코드Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual Tokens
https://github.com/unibester/age
공식 코드Attribute Group Editing for Reliable Few-shot Image Generation
https://github.com/unibester/sage
공식 코드Stable Attribute Group Editing for Reliable Few-shot Image Generation
https://github.com/univ-esuty/noisecollage
공식 코드NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging
https://github.com/universome/inr-gan
공식 코드https://github.com/uoguelph-mlrg/ggm-metrics
공식 코드https://github.com/uoguelph-mlrg/instance_selection_for_gans
공식 코드https://github.com/user0407/CLUDA
공식 코드CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
https://github.com/usstdqq/deep-adaptive-sampling-mask
공식 코드https://github.com/usydnlp/VICTR
공식 코드VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Tasks
https://github.com/utcsilab/ambient-diffusion-mri
공식 코드https://github.com/utomm/mini-diffuse-actor
공식 코드Mini Diffuser: Fast Multi-task Diffusion Policy Training Using Two-level Mini-batches
https://github.com/uw-cmg/GAN-STEM-Conv2MultiSlice
공식 코드Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation
https://github.com/uw-madison-lee-lab/cobsat
공식 코드https://github.com/uw-madison-lee-lab/score-wasserstein
공식 코드Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance
https://github.com/v-laurent/vrd-image-text-generator
공식 코드Spanish TrOCR: Leveraging Transfer Learning for Language Adaptation
https://github.com/vainf/tinyfusion
공식 코드https://github.com/val-iisc/NoisyTwins
공식 코드NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs
https://github.com/val-iisc/Reflecting-Reality
공식 코드Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror Reflections
https://github.com/val-iisc/gSRGAN
공식 코드Improving GANs for Long-Tailed Data through Group Spectral Regularization
https://github.com/valeoai/ADVENT
공식 코드ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/DADA
공식 코드DADA: Depth-aware Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/SemanticPalette
공식 코드Semantic Palette: Guiding Scene Generation with Class Proportions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.