https://github.com/ouxiang-li/safe
공식 코드Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective
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/overnap/bs-vae
공식 코드Beta-Sigma VAE: Separating beta and decoder variance in Gaussian variational autoencoder
https://github.com/owenzlz/pal4inpaint
공식 코드https://github.com/owl-project/NVISII
공식 코드NViSII: A Scriptable Tool for Photorealistic Image Generation
https://github.com/oxcsml/constrained-diffusion
공식 코드https://github.com/oxygenlu/ratlip
공식 코드https://github.com/oysterqaq/umair-fps
공식 코드UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style
https://github.com/ozcelikfu/ic-gan_fmri_reconstruction
공식 코드https://github.com/p-lambda/composed_finetuning
공식 코드Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization
https://github.com/p-lambda/unlabeled_outputs
공식 코드Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization
https://github.com/paarthneekhara/facesignsdemo
공식 코드FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes
https://github.com/pablopalafox/npms
공식 코드https://github.com/paint4poem/paint4poem
공식 코드Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems
https://github.com/pajotarthur/unsupervised_image_inpaiting
공식 코드https://github.com/palandr123/diffusionu-netllm
공식 코드LLM-guided Instance-level Image Manipulation with Diffusion U-Net Cross-Attention Maps
https://github.com/pals-ttic/sjc
공식 코드Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation
https://github.com/panchengzhao/lake-red
공식 코드LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion
https://github.com/pangzecheung/dual-task-pose-transformer-network
공식 코드Exploring Dual-task Correlation for Pose Guided Person Image Generation
https://github.com/panqihe-zjut/SOEBench
공식 코드Towards Small Object Editing: A Benchmark Dataset and A Training-Free Approach
https://github.com/pantheon5100/pid_diffusion
공식 코드https://github.com/paoche11/gungnir
공식 코드Gungnir: Exploiting Stylistic Features in Images for Backdoor Attacks on Diffusion Models
https://github.com/para-lost/AutoPresent
공식 코드https://github.com/parklabml/dp-mepf
공식 코드Pre-trained Perceptual Features Improve Differentially Private Image Generation
https://github.com/pasquini-dario/OutDomainExamples
공식 코드https://github.com/patchwork53/asymmetricattack
공식 코드Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks
https://github.com/patrickrperrine/comp-choreo
공식 코드KP-RNN: A Deep Learning Pipeline for Human Motion Prediction and Synthesis of Performance Art
https://github.com/patrickwu2/Depth-Completion
공식 코드Indoor Depth Completion with Boundary Consistency and Self-Attention
https://github.com/paulaonta/blind-valm
공식 코드Improving the Efficiency of Visually Augmented Language Models
https://github.com/paullyonel/multileveldiff
공식 코드Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation
https://github.com/pbontrager/GenerativePlayingNetworks
공식 코드https://github.com/pegahs1993/whisper-afe-talkingheadsgen
공식 코드Comparative Analysis of Audio Feature Extraction for Real-Time Talking Portrait Synthesis
https://github.com/peiman-m/ACQNP
공식 코드https://github.com/peirong26/PEPSI
공식 코드PEPSI: Pathology-Enhanced Pulse-Sequence-Invariant Representations for Brain MRI
https://github.com/peiyu-georgia-li/cheffusion-multimodal-foundation-model-integrating-recipe-and-food-image-generation
공식 코드ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation
https://github.com/pemami4911/segregate-relate-imagine
공식 코드Towards Improving the Generation Quality of Autoregressive Slot VAEs
https://github.com/pengbo807/ConditionVideo
공식 코드ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.