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Die Eule's Projects

pytorch-memonger icon pytorch-memonger

Sublinear memory optimization for deep learning. https://arxiv.org/abs/1604.06174

pytorch-pretrained-bert icon pytorch-pretrained-bert

A PyTorch implementation of Google AI's BERT model provided with Google's pre-trained models, examples and utilities.

pytorch_tabular icon pytorch_tabular

A standard framework for modelling Deep Learning Models for tabular data

randwirenn icon randwirenn

Implementation of: "Exploring Randomly Wired Neural Networks for Image Recognition"

reenactgan icon reenactgan

[ECCV 2018] ReenactGAN: Learning to Reenact Faces via Boundary Transfer

repvgg icon repvgg

RepVGG: Making VGG-style ConvNets Great Again

rulefit icon rulefit

Python implementation of the rulefit algorithm

selectiongan icon selectiongan

[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

selfie2anime icon selfie2anime

Anime2Selfie Backend Services - Lambda, Queue, API Gateway and traffic processing

sg2im icon sg2im

Code for "Image Generation from Scene Graphs", Johnson et al, CVPR 2018

siammask icon siammask

[CVPR2019] Fast Online Object Tracking and Segmentation: A Unifying Approach

sign-languge-to-speech-conversion icon sign-languge-to-speech-conversion

We help the deaf and the dumb to communicate with normal people using hand gesture to speech conversion. In this code we use depth maps from the kinect camera and techniques like convex hull + contour mapping to recognise 5 hand signs

simd icon simd

C++ image processing library with using of SIMD: SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, AVX, AVX2, AVX-512, VMX(Altivec) and VSX(Power7), NEON for ARM.

spade icon spade

Semantic Image Synthesis with SPADE

srgan-pytorch icon srgan-pytorch

A PyTorch implementation of SRGAN specific for Anime Super Resolution based on "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network". And another PyTorch WGAN-gp implementation of SRGAN referring to "Improved Training of Wasserstein GANs".

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