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Image Generation

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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/louaaron/Reflected-Diffusion

공식 코드

Reflected Diffusion Models

https://github.com/louisYen/Gen4Gen

공식 코드

Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition

https://github.com/louisbearing/hmo-audio

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A Comprehensive Multi-scale Approach for Speech and Dynamics Synchrony in Talking Head Generation

https://github.com/louisbearing/unconditionalheadmotion

공식 코드

Autoregressive GAN for Semantic Unconditional Head Motion Generation

https://github.com/ls4gan/benchmarking

공식 코드

UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation

https://github.com/ls4gan/uvcgan

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UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation

https://github.com/ls4gan/uvcgan2

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UVCGAN v2: An Improved Cycle-Consistent GAN for Unpaired Image-to-Image Translation

https://github.com/lsflyt-pku/deepcallifont

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DeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative Models

https://github.com/lsl001006/zone

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ZONE: Zero-Shot Instruction-Guided Local Editing

https://github.com/lslrh/cpsl

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Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation

https://github.com/lsplx/patcher

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Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement

https://github.com/lsx0101/Instant-NeRF-Stylization

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Instant Photorealistic Neural Radiance Fields Stylization

https://github.com/lth14/mage

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MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis

https://github.com/lth14/mar

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Autoregressive Image Generation without Vector Quantization

https://github.com/ltu-machine-learning/analysis-of-deep-perceptual-loss-networks

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A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions

https://github.com/luca2245/disarmpp_harmonization

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DISARM++: Beyond scanner-free harmonization

https://github.com/lucabutera/graphose_ocrrig

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Object-Centric Relational Representations for Image Generation

https://github.com/lucatirel/pix2pix-wgangp

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Novel Hybrid Integrated Pix2Pix and WGAN Model with Gradient Penalty for Binary Images Denoising

https://github.com/luchengthu/dpm-solver

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DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

https://github.com/lucidrains/imagen-pytorch

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Tell2Design: A Dataset for Language-Guided Floor Plan Generation

https://github.com/lucidrains/retrieval-augmented-ddpm

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Semi-Parametric Neural Image Synthesis

https://github.com/luckycallor/CDAAE

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Cross-Domain Adversarial Auto-Encoder

https://github.com/ludvb/nkca

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Learning Stationary Markov Processes with Contrastive Adjustment

https://github.com/luigisigillo/gwit

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Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models

https://github.com/luigisigillo/shipinsight

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Ship in Sight: Diffusion Models for Ship-Image Super Resolution

https://github.com/lukaslaobeyer/token-opt

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Highly Compressed Tokenizer Can Generate Without Training

https://github.com/lukasstruppek/class_attribute_inference_attacks

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Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations

https://github.com/lukasstruppek/exploiting-cultural-biases-via-homoglyphs

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Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis

https://github.com/lukasstruppek/rickrolling-the-artist

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Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis

https://github.com/lukasstruppek/the-biased-artist

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Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis

https://github.com/luked12/crop-growth-cgan

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Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial Networks

https://github.com/lukemelas/fixed-point-diffusion-models

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Fixed Point Diffusion Models

https://github.com/lukemelas/pixmatch

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PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training

https://github.com/luo-lorry/Stylized-Medical-Segmentation

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Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation

https://github.com/luo-yihong/yoso

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You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs

https://github.com/luo3300612/lare

공식 코드

LaRE^2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.