MADONOKOUKI

Block-wise-Scrambled-Image-Recognition

Code for Adaptation Network introduced in "Block-wise Scrambled Image Recognition Using Adaptation Network" paper (AAAI WS 2020)

faster_rcnn_pytorch

Faster RCNN with PyTorch

gcce2022_instancewise_center_loss

psivt23_scramblemix

SPG_EI2020

Code of experiments in "Scrambling Parameter Generation to Improve Perceptual Information Hiding" paper (EI 2020)

MADONOKOUKI/gcce2022_instancewise_center_loss

MADONOKOUKI/psivt23_scramblemix

MADONOKOUKI/SPG_EI2020

Code of experiments in "Scrambling Parameter Generation to Improve Perceptual Information Hiding" paper (EI 2020)

MADONOKOUKI/cv-snippets

My code snippets for computer vision.

MADONOKOUKI/dda

Differentiable Data Augmentation Library

MADONOKOUKI/diffvg

Differentiable Vector Graphics Rasterization

MADONOKOUKI/ICCV2019-LearningToPaint

ICCV2019 - A painting AI that can reproduce paintings stroke by stroke using deep reinforcement learning.

MADONOKOUKI/InstaHide

InstaHide: Instance-hiding Schemes for Private Distributed Learning

MADONOKOUKI/lambda-networks

Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute

MADONOKOUKI/LIVE-Layerwise-Image-Vectorization

[CVPR 2022 Oral] Towards Layer-wise Image Vectorization

MADONOKOUKI/MADONOKOUKI.github.io

MADONOKOUKI/Medium-Tutorials

MADONOKOUKI/mini-imagenet-tools

Tools for generating mini-ImageNet dataset and processing batches

MADONOKOUKI/ned_ros

Ned ros stack

MADONOKOUKI/neural_real_estate

MADONOKOUKI/niryo_one_ros

Niryo One ROS stack

MADONOKOUKI/Open3D

Open3D: A Modern Library for 3D Data Processing

MADONOKOUKI/opencv

Open Source Computer Vision Library

MADONOKOUKI/pix2pixHD

Synthesizing and manipulating 2048x1024 images with conditional GANs

MADONOKOUKI/privacy

Library for training machine learning models with privacy for training data

MADONOKOUKI/pytorch-classification

Classification with PyTorch.

MADONOKOUKI/Random-Shadows-Highlights

A new data augmentation method for extreme lighting conditions.

MADONOKOUKI/RepDistiller

[ICLR 2020] Contrastive Representation Distillation (CRD), and benchmark of recent knowledge distillation methods

MADONOKOUKI/rigid_transform_3D

MADONOKOUKI/segraph

The segraph library creates graphs from SLIC superpixels. It can be used for using CRF for image segmentation https://pypi.python.org/pypi/segraph/0.5

MADONOKOUKI/SIA-GAN

MADONOKOUKI/stylized-neural-painting

Official Pytorch implementation of the preprint paper "Stylized Neural Painting", in CVPR 2021.

MADONOKOUKI/SupContrast

PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)

MADONOKOUKI/vision

Datasets, Transforms and Models specific to Computer Vision

MADONOKOUKI/vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch