Spatial Transformer Networks (STN) implementation in TensorFlow
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Updated
Jul 31, 2019 - Python
Spatial Transformer Networks (STN) implementation in TensorFlow
My thesis code for Traffic Sign Recognition using 2 different datasets (GTSRB and DFG) and different kinds of models (CNN, STN, ViT).
This study aims to show that group equivariant CNNs outperform spatial transformers, on tasks which demand rotation invariance, by providing theoretical background and experimental performance comparison with detailed analysis.
ML framework to estimate Bayesian posteriors of galaxy morphological parameters
Foveated Spatial Transformers
Recognizing traffic signs with deep learning and PyTorch using Spatial Transformer Convolutional Neural Networks.
Implementation of STN (Spatial Transformer Network) and ICSTN (Inverse Compositional Spatial Transformer Networks) in Tensorlayer to predict transformation parameters from 2D images.
Image, point set, and surface registration in PyTorch.
Unofficial PyTorch implementation for incorporating a 3D Morphable Model (3DMM) into a Spatial Transformer Network (STN)
Image-and-Spatial Transformer Networks
Code for the paper "KISS: Keeping it Simple for Scene Text Recognition"
An unofficial PyTorch implementation of VoxelMorph- An unsupervised 3D deformable image registration method
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