https://github.com/vinairesearch/creps
공식 코드Efficient Scale-Invariant Generator with Column-Row Entangled Pixel Synthesis
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 23
- 평가표
- 93
- 보존된 평가 기록
- 870
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/vinairesearch/dimsum
공식 코드DiMSUM: Diffusion Mamba -- A Scalable and Unified Spatial-Frequency Method for Image Generation
https://github.com/vinairesearch/lfm
공식 코드https://github.com/vinairesearch/tise-toolbox
공식 코드https://github.com/vinairesearch/wavediff
공식 코드Wavelet Diffusion Models are fast and scalable Image Generators
https://github.com/vincentlux/cigli
공식 코드https://github.com/vincentstimper/normalizing-flows
공식 코드https://github.com/vincentstimper/resampled-base-flows
공식 코드https://github.com/vineetkumarrakesh/thg
공식 코드https://github.com/vinhsuhi/ld3
공식 코드https://github.com/vinid/text-to-image-bias
공식 코드Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
https://github.com/vinojjayasundara/textcaps
공식 코드TextCaps : Handwritten Character Recognition with Very Small Datasets
https://github.com/vinthony/s2am
공식 코드Improving the Harmony of the Composite Image by Spatial-Separated Attention Module
https://github.com/viola-siemens/general-image-anonymization
공식 코드SVIA: A Street View Image Anonymization Framework for Self-Driving Applications
https://github.com/vios-s/csdisentanglement_metrics_library
공식 코드Measuring the Biases and Effectiveness of Content-Style Disentanglement
https://github.com/virajprabhu/lance
공식 코드LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
https://github.com/virobo-15/gmnr
공식 코드Generative Multiplane Neural Radiance for 3D-Aware Image Generation
https://github.com/virobo-15/mgcc
공식 코드https://github.com/virobo-15/xm-gan
공식 코드Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification
https://github.com/vishal3477/proactive_imd
공식 코드https://github.com/vishwa91/deepir
공식 코드https://github.com/visinf/da-sac
공식 코드Self-supervised Augmentation Consistency for Adapting Semantic Segmentation
https://github.com/visinf/diagen
공식 코드https://github.com/visinf/fldr-vfi
공식 코드Efficient Feature Extraction for High-resolution Video Frame Interpolation
https://github.com/visinf/lnfmm
공식 코드Latent Normalizing Flows for Many-to-Many Cross-Domain Mappings
https://github.com/visinf/mar-scf
공식 코드https://github.com/vislearn/ControlNet-XS
공식 코드ControlNet-XS: Rethinking the Control of Text-to-Image Diffusion Models as Feedback-Control Systems
https://github.com/visual-words/visual-words
공식 코드https://github.com/vita-epfl/code
공식 코드https://github.com/vita-epfl/collaborative-gan-sampling
공식 코드https://github.com/vita-epfl/semdisc
공식 코드A Shared Representation for Photorealistic Driving Simulators
https://github.com/vita-group/font_completion_graph
공식 코드Font Completion and Manipulation by Cycling Between Multi-Modality Representations
https://github.com/vita-group/neural-implicit-dict
공식 코드https://github.com/vita-group/ssharmonization
공식 코드https://github.com/vivitsai/PiiGAN
공식 코드PiiGAN: Generative Adversarial Networks for Pluralistic Image Inpainting
https://github.com/vivivic/speech-domain-adaptation-DRL
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.