A Tensorflow implementation of Spatial Transformer Networks.
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Updated
Jun 2, 2018 - Python
A Tensorflow implementation of Spatial Transformer Networks.
PyTorch implementation of Spatial Transformer Network (STN) with Thin Plate Spline (TPS)
🐝Tensorflow Implementation of Spatial Transformer Networks
The source code for paper "Deep Image Spatial Transformation for Person Image Generation"
MatConvNet implementation for incorporating a 3D Morphable Model (3DMM) into a Spatial Transformer Network (STN)
implement CRNN in Keras with Spatial Transformer Network
Lightweight CRNN for OCR (including handwritten text) with depthwise separable convolutions and spatial transformer module [keras+tf]
DeepWarp for Facial Expression Manipulation
Spatial Transformer Networks. Refer to daviddao/spatial-transformer-tensorflow.
Built and trained a deep neural network to classify traffic signs, using PyTorch. The highlights of this solution would be data preprocessing, trained with heavily augmented data and using Spatial Transformer Network.
Large-scale 3D image registration based on spatial transformers and stacked convolutional autoencoders
Diversity in Fashion Recommendation Using Semantic Parsing
CRNN(Convolutional Recurrent Neural Network), with optional STN(Spatial Transformer Network), in Tensorflow, multi-gpu supported.
This repository provides a Colab Notebook that shows how to use Spatial Transformer Networks inside CNNs in Keras.
Experiments on cluttered mnist dataset with Tensorflow.
Spatial Transformer Nets in TensorFlow/ TensorLayer
PyTorch implementation of the descriptor DEAL presented at NeurIPS 2021 "Extracting Deformation-Aware Local Features by Learning to Deform".
Code for the paper entitled "Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods".
Pytorch Implementation of Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods
Codebase for "Decoding language spatial relations to 2D spatial arrangements" (Findings of EMNLP 2020).
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