Data Efficient Gans
mit-han-lab
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
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Results: 17
mit-han-lab
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
ZPdesu
SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)
harskish
Discovering Interpretable GAN Controls [NeurIPS 2020]
FoundationVision
[NeurIPS 2024 Best Paper Award][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation…
sooftware
[Unofficial] PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)
FoundationVision
[NeurIPS 2025 Oral]Infinity⭐️: Unified Spacetime AutoRegressive Modeling for Visual Generation
amanchadha
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in…
OpenDriveLab
[NeurIPS 2024] A Generalizable World Model for Autonomous Driving
FoundationVision
[NeurIPS 2025 Spotlight] A Unified Tokenizer for Visual Generation and Understanding
caiyuanhao1998
OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions (NeurIPS 2025)
OndrejTexler
The official implementation of our SIGGRAPH 2020 paper Interactive Video Stylization Using Few-Shot Patch-Based Training
onion-liu
Official PyTorch implementation of "BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation" (NeurIPS 2021)
aHapBean
[NeurIPS 2025] VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models
thuml
Official repository for "RLVR-World: Training World Models with Reinforcement Learning" (NeurIPS 2025), https://arxiv.org/abs/2505.13934
H-EmbodVis
[NeurIPS 2025] More Than Generation: Unifying Generation and Depth Estimation via Text-to-Image Diffusion Models
thuml
Official repository for "iVideoGPT: Interactive VideoGPTs are Scalable World Models" (NeurIPS 2024), https://arxiv.org/abs/2405.15223
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