https://github.com/qy-h00/conceptrol
공식 코드Conceptrol: Concept Control of Zero-shot Personalized Image Generation
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/r-three/realistic_evaluation_of_model_merging_for_compositional_generalization
공식 코드Realistic Evaluation of Model Merging for Compositional Generalization
https://github.com/raeidsaqur/CapsGAN
공식 코드CapsGAN: Using Dynamic Routing for Generative Adversarial Networks
https://github.com/ragor114/InsertDiffusion
공식 코드https://github.com/rail-berkeley/soar
공식 코드Autonomous Improvement of Instruction Following Skills via Foundation Models
https://github.com/rainorangelemon/complift
공식 코드Improving Compositional Generation with Diffusion Models Using Lift Scores
https://github.com/rajatrasal/diffusion-counterfactuals
공식 코드https://github.com/rajhans0/poly_inr
공식 코드Polynomial Implicit Neural Representations For Large Diverse Datasets
https://github.com/rajiv1990/DEff-GAN-Data-Efficient-GAN
공식 코드DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesis
https://github.com/rajiv1990/Unsupervised-Cross-domain-I2I-using-a-Shared-Discriminator
공식 코드Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator
https://github.com/raman1121/diffusion_memorization_hpo
공식 코드MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection
https://github.com/rand2ai/fissionvae
공식 코드FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decomposition
https://github.com/ranery/Bayesian-CycleGAN
공식 코드Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling
https://github.com/rang1991/public-intermediate-semantics-for-generation
공식 코드Generating Intermediate Representations for Compositional Text-To-Image Generation
https://github.com/rapidsathkust/vic-ddpm
공식 코드A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction
https://github.com/rapisurazurite/diffface
공식 코드DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis
https://github.com/raven38/ossgan
공식 코드https://github.com/ravinewindteer/glpdepth-edited
공식 코드Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep Learning
https://github.com/ravinewindteer/shapenetsem-to-rgbd
공식 코드Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep Learning
https://github.com/razvanmarinescu/brgm
공식 코드https://github.com/razvanmarinescu/brgm-pytorch
공식 코드https://github.com/razvanmatisan/duodiff
공식 코드DuoDiff: Accelerating Diffusion Models with a Dual-Backbone Approach
https://github.com/realcrane/iPLAN-Interactive-and-Procedural-Layout-Planning
공식 코드https://github.com/recklessronan/muse
공식 코드https://github.com/redaigc/storymaker
공식 코드StoryMaker: Towards Holistic Consistent Characters in Text-to-image Generation
https://github.com/redorangeyellowy/AttentionHand
공식 코드AttentionHand: Text-driven Controllable Hand Image Generation for 3D Hand Reconstruction in the Wild
https://github.com/reds-lab/restricted_gradient_diversity_unlearning
공식 코드Boosting Alignment for Post-Unlearning Text-to-Image Generative Models
https://github.com/reedscot/icml2016
공식 코드https://github.com/reina0112/phismid-phiswid
공식 코드PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D Images
https://github.com/renanrojasg/adv_robust_autoencoder
공식 코드https://github.com/renderme-360/renderme-360
공식 코드RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
https://github.com/renhaa/semantic-diffusion
공식 코드Discovering Interpretable Directions in the Semantic Latent Space of Diffusion Models
https://github.com/renyurui/neural-texture-extraction-distribution
공식 코드Neural Texture Extraction and Distribution for Controllable Person Image Synthesis
https://github.com/renyurui/pirender
공식 코드PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering
https://github.com/research4pan/plum
공식 코드https://github.com/researchcode001/divide-and-conquer-attack
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.