https://github.com/manicman1999/StyleGAN-Keras
A Style-Based Generator Architecture for Generative Adversarial Networks
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Disentanglement
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This is an approach to solve a diverse set of tasks in a data efficient manner by disentangling (or isolating ) the underlying structure of the main problem into disjoint parts of its representations. This disentanglement can be done by focussing on the "transformation" properties of the world(main problem)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/manicman1999/StyleGAN-Tensorflow-2.0
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/mantonios107/attrs-fr-embs
https://github.com/martiansideofthemoon/style-transfer-paraphrase
https://github.com/maxjiang93/ShapeFlow
https://github.com/mcharrak/discreteVAE
Isolating Sources of Disentanglement in Variational Autoencoders
https://github.com/merveenoyan/siglip
https://github.com/mgmk2/StyleGAN
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/microsoft/DiscoFaceGAN
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning
https://github.com/microsoft/qlib/tree/main/examples/benchmarks/ADD
ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting
https://github.com/mk-minchul/cse802_face_augmentation
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning
https://github.com/mkulaczkowski/mixedstylegan
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/mlfoundations/open_clip
https://github.com/mli0603/blenderneuralangelo
https://github.com/mmrl/disent-and-gen
https://github.com/mmrl/lost-in-latent-space
https://github.com/modelscope/3D-Speaker
https://github.com/modelscope/swift
Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone
https://github.com/mokeam/StatueStyleGAN
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/morrisfl/unifex
https://github.com/nicolasigor/FactorVAE
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmgeneration
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/optas/shapeglot
https://github.com/orenkatzir/VN-SPD
Shape-Pose Disentanglement using SE(3)-equivariant Vector Neurons
https://github.com/oripress/ContentDisentanglement
Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
https://github.com/pacifinapacific/StyleGAN_LatentEditor
Interpreting the Latent Space of GANs for Semantic Face Editing
https://github.com/pb2377/PyTorch-Disentangling-Content-and-Style-Unsupervised
Disentangling Content and Style via Unsupervised Geometry Distillation
https://github.com/pengfudan/DisentangledFaceGAN
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning
https://github.com/perladoubinsky/balanced_sampling_gan_controls
Multi-Attribute Balanced Sampling for Disentangled GAN Controls
https://github.com/perplexingpegasus/EarthLandscapeGAN
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/pfnet-research/chainer-stylegan
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/pierrickch/textureautoencoder
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/pluu2/SeparationFactor
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
https://github.com/podgorskiy/StyleGAN_Blobless
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/podgorskiy/StyleGan
A Style-Based Generator Architecture for Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.