The following pages link to SegNet (Q39291):
Displaying 34 items.
- Subset selection for visualization of relevant image fractions for deep learning based semantic image segmentation (Q1661471) (← links)
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification (Q1721865) (← links)
- Credit spread approximation and improvement using random forest regression (Q1735198) (← links)
- Training of deep neural networks for the generation of dynamic movement primitives (Q1982413) (← links)
- Automatic extraction of cell nuclei using dilated convolutional network (Q2028918) (← links)
- A multi-stage deep learning based algorithm for multiscale model reduction (Q2029406) (← links)
- OCNet: object context for semantic segmentation (Q2054412) (← links)
- A view of computational models for image segmentation (Q2084578) (← links)
- Semi-supervised semantic segmentation in Earth observation: the MiniFrance suite, dataset analysis and multi-task network study (Q2102368) (← links)
- Deep convolutional neural networks with spatial regularization, volume and star-shape priors for image segmentation (Q2103871) (← links)
- Improved clustering algorithms for image segmentation based on non-local information and back projection (Q2123512) (← links)
- Nonlocal regularized CNN for image segmentation (Q2198002) (← links)
- BCR-net: A neural network based on the nonstandard wavelet form (Q2214634) (← links)
- A review on deep learning in medical image reconstruction (Q2218098) (← links)
- Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems (Q2222519) (← links)
- Self-supervised model adaptation for multimodal semantic segmentation (Q2226186) (← links)
- Seismic stratum segmentation using an encoder-decoder convolutional neural network (Q2238122) (← links)
- A multiscale neural network based on hierarchical nested bases (Q2319969) (← links)
- U-Net architecture variants for brain tumor segmentation of histogram corrected images (Q2674151) (← links)
- Robust region-based active contour models via local statistical similarity and local similarity factor for intensity inhomogeneity and high noise image segmentation (Q2674896) (← links)
- Application of Bayesian generative adversarial networks to geological facies modeling (Q2676501) (← links)
- Multi-scale fusion network: a new deep learning structure for elliptic interface problems (Q2691986) (← links)
- <italic>Her2Net</italic>: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation (Q4617213) (← links)
- Dynamic Feature Matching for Partial Face Recognition (Q4617939) (← links)
- CAD Model Segmentation Via Deep Learning (Q4987311) (← links)
- Predicting peak stresses in microstructured materials using convolutional encoder–decoder learning (Q5041623) (← links)
- A Comprehensive Review of Modern Object Segmentation Approaches (Q5046687) (← links)
- Semantic Image Segmentation: Two Decades of Research (Q5046690) (← links)
- Efficient Deep Learning Framework with Group Convolution for Segmentation of Histopathology Image (Q5050282) (← links)
- STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing (Q5105480) (← links)
- Deep learning for ranking response surfaces with applications to optimal stopping problems (Q5139253) (← links)
- GAN-Based Priors for Quantifying Uncertainty in Supervised Learning (Q5158923) (← links)
- A Multiscale Neural Network Based on Hierarchical Matrices (Q5222107) (← links)
- A regularized convolutional neural network for semantic image segmentation (Q5856266) (← links)
