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dlyzhou's Projects

a_v_uncertain icon a_v_uncertain

Code for our ISBI 2019 paper on Artery/Vein classification with uncertainty predictions

acol icon acol

Adversarial Complementary Learning for Weakly Supervised Object Localization

adl icon adl

Attention-based Dropout Layer for Weakly Supervised Object Localization, CVPR 2019 (Oral)

alibi icon alibi

Algorithms for monitoring and explaining machine learning models

amazing-semantic-segmentation icon amazing-semantic-segmentation

Amazing Semantic Segmentation on Tensorflow && Keras (include FCN, UNet, SegNet, PSPNet, PAN, RefineNet, DeepLabV3, DeepLabV3+, DenseASPP, BiSegNet)

arl-eegmodels icon arl-eegmodels

This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

augmentor icon augmentor

Image augmentation library in Python for machine learning.

awesome-object-detection icon awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

brainstorm icon brainstorm

Implementation of "Data augmentation using learned transforms for one-shot medical image segmentation"

brats2018-tumor-segmentation icon brats2018-tumor-segmentation

We provide DeepMedic and 3D UNet in pytorch for brain tumore segmentation. We also integrate location information with DeepMedic and 3D UNet by adding additional brain parcellation with original MR images.

cam icon cam

Class Activation Mapping

catdcgan icon catdcgan

A DCGAN that generate Cat pictures 🐱‍💻

ce-net icon ce-net

The manuscript has been accepted in TMI.

chinese_ocr icon chinese_ocr

CTPN + DenseNet + CTC based end-to-end Chinese OCR implemented using tensorflow and keras

cleverhans icon cleverhans

An adversarial example library for constructing attacks, building defenses, and benchmarking both

clip icon clip

Contrastive Language-Image Pretraining

covid-19 icon covid-19

A collection of work related to COVID-19

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