https://github.com/cindyxinyiwang/deep-latent-sequence-model
공식 코드A Probabilistic Formulation of Unsupervised Text Style Transfer
Papers with Code 아카이브
Drawing Pictures
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/clovaai/WCT2
공식 코드https://github.com/cmry/style-obfuscation
공식 코드https://github.com/comp-syn/comp-syn
공식 코드Divergences in Color Perception between Deep Neural Networks and Humans
https://github.com/computer-vision2022/pama
공식 코드https://github.com/cookielee77/DAST
공식 코드https://github.com/crosszamirski/guided-i2i
공식 코드Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels
https://github.com/crowddynamicslab/styleinfusion
공식 코드Audience-Centric Natural Language Generation via Style Infusion
https://github.com/cxa9264/confeti
공식 코드https://github.com/cyclomon/clipstyler
공식 코드CLIPstyler: Image Style Transfer with a Single Text Condition
https://github.com/czczup/URST
공식 코드Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization
https://github.com/czczup/block-shuffle
공식 코드Block Shuffle: A Method for High-resolution Fast Style Transfer with Limited Memory
https://github.com/dada-bots/dadaGP
공식 코드DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models
https://github.com/damo-vilab/composer
공식 코드Composer: Creative and Controllable Image Synthesis with Composable Conditions
https://github.com/danifranco/em_domain_adaptation
공식 코드Deep learning based domain adaptation for mitochondria segmentation on EM volumes
https://github.com/danruta/neat
공식 코드https://github.com/dapangliu/lamer
공식 코드Non-Parallel Text Style Transfer with Self-Parallel Supervision
https://github.com/davidmhart/interpolated-selectionconv
공식 코드Interpolated SelectionConv for Spherical Images and Surfaces
https://github.com/dayihengliu/Fine-Grained-Style-Transfer
공식 코드Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning
https://github.com/decurtoidiaz/ce4
공식 코드Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from Chang'E-4
https://github.com/dengpingfan/fsgan
공식 코드https://github.com/devavrattomar/sst
공식 코드Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation
https://github.com/dingsheng-ong/ipr-gan
공식 코드Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack
https://github.com/diyiiyiii/StyTR-2
공식 코드https://github.com/dkadish/Style-Transfer-for-Object-Detection-in-Art
공식 코드Improving Object Detection in Art Images Using Only Style Transfer
https://github.com/dongqi-fan/irconstyle
공식 코드IRConStyle: Image Restoration Framework Using Contrastive Learning and Style Transfer
https://github.com/drscotthawley/SPNet
공식 코드ConvNets for Counting: Object Detection of Transient Phenomena in Steelpan Drums
https://github.com/duyuwen-duen/roco-sim
공식 코드RoCo-Sim: Enhancing Roadside Collaborative Perception through Foreground Simulation
https://github.com/dvs-whu/E-SAI
공식 코드Event-based Synthetic Aperture Imaging with a Hybrid Network
https://github.com/dykang/PASTEL
공식 코드https://github.com/echigot/cactif
공식 코드Style Transfer with Diffusion Models for Synthetic-to-Real Domain Adaptation
https://github.com/eezkni/fdl
공식 코드Misalignment-Robust Frequency Distribution Loss for Image Transformation
https://github.com/elbria/xformal-fost-meta
공식 코드https://github.com/emmanueliarussi/generative3DSpongiosa
공식 코드Generative Modelling of 3D in-silico Spongiosa with Controllable Micro-Structural Parameters
https://github.com/emreaksan/deepwriting
공식 코드DeepWriting: Making Digital Ink Editable via Deep Generative Modeling
https://github.com/endywon/aesust
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.