The following pages link to CycleGAN (Q40016):
Displaying 25 items.
- Generating universal adversarial perturbation with ResNet (Q2666824) (← links)
- Imperfect imaGANation: implications of GANs exacerbating biases on facial data augmentation and snapchat face lenses (Q2667840) (← links)
- Born machine model based on matrix product state quantum circuit (Q2669398) (← links)
- A convenient infinite dimensional framework for generative adversarial learning (Q2683193) (← links)
- A physics-informed diffusion model for high-fidelity flow field reconstruction (Q2687536) (← links)
- LBCRN: lightweight bidirectional correction residual network for image super-resolution (Q2688039) (← links)
- SRV-GAN: a generative adversarial network for segmenting retinal vessels (Q2688735) (← links)
- A peridynamic-informed neural network for continuum elastic displacement characterization (Q2693390) (← links)
- Using image processing techniques in computational mechanics (Q2697790) (← links)
- Multiview Alignment and Generation in CCA via Consistent Latent Encoding (Q3386409) (← links)
- Accurate prediction of the particle image velocimetry flow field and rotor thrust using deep learning (Q3390379) (← links)
- (Q4999018) (← links)
- (Q5019307) (← links)
- Matching Component Analysis for Transfer Learning (Q5027030) (← links)
- Semantic Image Segmentation: Two Decades of Research (Q5046690) (← links)
- Wide-Band Butterfly Network: Stable and Efficient Inversion Via Multi-Frequency Neural Networks (Q5050440) (← links)
- Optimal Transport Driven CycleGAN for Unsupervised Learning in Inverse Problems (Q5143340) (← links)
- Unsupervised deep learning for super-resolution reconstruction of turbulence (Q5145486) (← links)
- (Q5148969) (← links)
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations (Q5214836) (← links)
- (Q5381139) (← links)
- Shared Prior Learning of Energy-Based Models for Image Reconstruction (Q5860383) (← links)
- Deep Learning for Multimedia Forensics (Q5863769) (← links)
- Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems (Q5873926) (← links)
- A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers (Q5878764) (← links)
