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Jesper Kers's Projects

ai-ffpe icon ai-ffpe

Deep Learning-based Frozen Section to FFPE Translation

artifact icon artifact

Generation of synthetic artefacts / digital pathology

attention-gated-networks icon attention-gated-networks

Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

attentiondeepmil icon attentiondeepmil

Implementation of Attention-based Deep Multiple Instance Learning in PyTorch

ax icon ax

Adaptive Experimentation Platform

big_transfer icon big_transfer

Official repository for the "Big Transfer (BiT): General Visual Representation Learning" paper.

byol icon byol

Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

byol-pytorch icon byol-pytorch

Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

calibration icon calibration

Python package for evaluating model calibration in classification

candia icon candia

Canonical Decomposition of Data-Independent-Acquired Spectra

captum icon captum

Model interpretability and understanding for PyTorch

clairvoyance icon clairvoyance

Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series

clam icon clam

Data Efficient & Weakly Supervised Computational Pathology on Whole Slide Images

cutout icon cutout

2.56%, 15.20%, 1.30% on CIFAR10, CIFAR100, and SVHN https://arxiv.org/abs/1708.04552

deepcluster icon deepcluster

Deep Clustering for Unsupervised Learning of Visual Features

deepercluster icon deepercluster

Implements the unsupervised pre-training of convolutional neural networks

deepmind-research icon deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

deepspeed icon deepspeed

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

detectron2 icon detectron2

Detectron2 is FAIR's next-generation platform for object detection and segmentation.

diann icon diann

DIA-NN - a fast and easy to use tool for processing data-independent acquisition (DIA) proteomics data

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