https://github.com/hichoe95/Artifact-Detection-and-Sequential-Ablation
A Style-Based Generator Architecture for Generative Adversarial Networks
Papers with Code 아카이브
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/hmatty/stylized-neural-painting
https://github.com/huangzh13/StyleGAN.pytorch
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/huawei-noah/trustworthyAI
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
https://github.com/huggingface/diffusers
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model
https://github.com/huggingface/transformers
https://github.com/hui-po-wang/hijackgan
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/ialhashim/StyleGAN-Tensorflow2
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/iamkanghyunchoi/ait
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
https://github.com/ilkhem/icebeem
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
https://github.com/ilkhem/ivae
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
https://github.com/ilyatrofimov/sftd
Representation Topology Divergence: A Method for Comparing Neural Network Representations
https://github.com/inconnu11/Objective-evaluation_speech_synthesis
Speech Resynthesis from Discrete Disentangled Self-Supervised Representations
https://github.com/irynakostyshyn/A-Style-Based-Generator-Architecture-for-GAN
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/isaacschaal/SG_training
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/itsuki8914/stylegan-TensorFlow
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/jarvisWang0903/Paper-Useful
Theory and Evaluation Metrics for Learning Disentangled Representations
https://github.com/jiazhi412/strong_attribute_bias
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/jimzers/stylegan2-colabs
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/jiupinjia/stylized-neural-painting
https://github.com/jixinya/EVP
https://github.com/jlian2/Robust-Voice-Style-Transfer
Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion
https://github.com/joanfmendo/MixNMatch
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
https://github.com/jooyae/NVIDIA_STYLEGAN
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/junaidwahid/Implementation-of-StyleGan_A-Style-Based-Generator-Architecture-for-Generative-Adversarial-Networks
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/kamwoh/partcraft
DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination
https://github.com/keras-team/keras-io/blob/master/examples/generative/stylegan.py
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/khurram702/StyleBasedGAN
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/kondratevakate/fmri-component-analysis
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
https://github.com/krantirk/-Adversarial-Latent-Autoencoders
https://github.com/kzhou23/shape_pose_disent
https://github.com/lijiaman/egoego_release
https://github.com/lioryariv/volsdf
https://github.com/liuem607/expVAE
https://github.com/llvqi/multiview_and_self-supervision
https://github.com/lucidrains/transganformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.