Search Results for author: Christoph Meinel

Found 39 papers, 19 papers with code

Weakly Supervised Scene Text Detection using Deep Reinforcement Learning

1 code implementation13 Jan 2022 Emanuel Metzenthin, Christian Bartz, Christoph Meinel

In this paper we propose a weak supervision method for scene text detection, which makes use of reinforcement learning (RL).

reinforcement-learning Scene Text Detection

BoolNet: Streamlining Binary Neural Networks Using Binary Feature Maps

no code implementations29 Sep 2021 Nianhui Guo, Joseph Bethge, Haojin Yang, Kai Zhong, Xuefei Ning, Christoph Meinel, Yu Wang

Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts, often based on specialized model designs using additional 32-bit components.

Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data

2 code implementations14 Jul 2021 Christian Bartz, Hendrik Raetz, Jona Otholt, Christoph Meinel, Haojin Yang

One of the most pressing problems in the automated analysis of historical documents is the availability of annotated training data.

Semantic Segmentation

BoolNet: Minimizing The Energy Consumption of Binary Neural Networks

1 code implementation13 Jun 2021 Nianhui Guo, Joseph Bethge, Haojin Yang, Kai Zhong, Xuefei Ning, Christoph Meinel, Yu Wang

Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts.

Mutual Distillation of Confident Knowledge

no code implementations2 Jun 2021 Ziyun Li, Xinshao Wang, Di Hu, Neil M. Robertson, David A. Clifton, Christoph Meinel, Haojin Yang

Additionally, CMD covers two special cases: zero-knowledge and all knowledge, leading to a unified MKD framework.

Knowledge Distillation

Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing

no code implementations22 Mar 2021 Gonçalo Mordido, Haojin Yang, Christoph Meinel

The analysis of the compression effects in generative adversarial networks (GANs) after training, i. e. without any fine-tuning, remains an unstudied, albeit important, topic with the increasing trend of their computation and memory requirements.

Quantization

Handwriting Classification for the Analysis of Art-Historical Documents

1 code implementation4 Nov 2020 Christian Bartz, Hendrik Rätz, Christoph Meinel

In this paper, we focus on the analysis of handwriting in scanned documents from the art-historic archive of the WPI.

Classification General Classification +2

One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGAN

1 code implementation21 Oct 2020 Christian Bartz, Joseph Bethge, Haojin Yang, Christoph Meinel

Generative Adversarial Networks (GANs) have achieved state-of-the-art performance for several image generation and manipulation tasks.

Image Denoising Image Generation

Improving the Evaluation of Generative Models with Fuzzy Logic

1 code implementation3 Feb 2020 Julian Niedermeier, Gonçalo Mordido, Christoph Meinel

Objective and interpretable metrics to evaluate current artificial intelligent systems are of great importance, not only to analyze the current state of such systems but also to objectively measure progress in the future.

Image Generation

MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?

1 code implementation16 Jan 2020 Joseph Bethge, Christian Bartz, Haojin Yang, Ying Chen, Christoph Meinel

However, the binarization of weights and activations leads to feature maps of lower quality and lower capacity and thus a drop in accuracy compared to traditional networks.

Binarization

microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination

no code implementations10 Jan 2020 Gonçalo Mordido, Haojin Yang, Christoph Meinel

We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, called microbatch, to each discriminator.

K-Metamodes: frequency- and ensemble-based distributed k-modes clustering for security analytics

no code implementations30 Sep 2019 Andrey Sapegin, Christoph Meinel

Besides this, the existing feature discretisation method from the previous work is utilised in order to adapt k-modes for processing of mixed data sets.

Intrusion Detection

Back to Simplicity: How to Train Accurate BNNs from Scratch?

no code implementations19 Jun 2019 Joseph Bethge, Haojin Yang, Marvin Bornstein, Christoph Meinel

Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs but suffer from substantial accuracy degradation compared to their real-valued counterparts on large-scale datasets, e. g., ImageNet.

Quantization

Towards Automatic Personality Prediction Using Facebook Like Categories

no code implementations11 Dec 2018 Raad Bin Tareaf, Philipp Berger, Patrick Hennig, Christoph Meinel

We demonstrate that effortlessly accessible digital records of behavior such as Facebook Likes can be obtained and utilized to automatically distinguish a wide range of highly delicate personal traits including: life satisfaction, cultural ethnicity, political views, age, gender and personality traits.

Training Competitive Binary Neural Networks from Scratch

1 code implementation5 Dec 2018 Joseph Bethge, Marvin Bornstein, Adrian Loy, Haojin Yang, Christoph Meinel

In our work, we focus on increasing the performance of binary neural networks without such prior knowledge and a much simpler training strategy.

Benchmark

Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data

no code implementations22 Nov 2018 Mina Rezaei, Haojin Yang, Christoph Meinel

We design a new conditional GAN with two components: a generative model and a discriminative model to mitigate imbalanced data problem through selective weighted loss.

Brain Tumor Segmentation Cardiac Segmentation +3

LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks

1 code implementation14 Nov 2018 Christian Bartz, Haojin Yang, Joseph Bethge, Christoph Meinel

Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student.

object-detection Weakly Supervised Object Detection

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation

no code implementations9 Oct 2018 Mina Rezaei, Haojin Yang, Christoph Meinel

We propose a new conditional generative refinement network with three components: a generative, a discriminative, and a refinement network to mitigate unbalanced data problem through ensemble learning.

Cell Segmentation Ensemble Learning +2

microGAN: Promoting Variety through Microbatch Discrimination

no code implementations27 Sep 2018 Goncalo Mordido, Haojin Yang, Christoph Meinel

We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, called microbatch, to each discriminator.

Learning to Train a Binary Neural Network

1 code implementation27 Sep 2018 Joseph Bethge, Haojin Yang, Christian Bartz, Christoph Meinel

In our work, we focus on increasing our understanding of the training process and making it accessible to everyone.

Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators

no code implementations30 Jul 2018 Gonçalo Mordido, Haojin Yang, Christoph Meinel

We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some probability at the end of each batch.

SEE: Towards Semi-Supervised End-to-End Scene Text Recognition

2 code implementations14 Dec 2017 Christian Bartz, Haojin Yang, Christoph Meinel

Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task.

Scene Text Detection Scene Text Recognition

Traversal-Free Word Vector Evaluation in Analogy Space

no code implementations WS 2017 Xiaoyin Che, Nico Ring, Willi Raschkowski, Haojin Yang, Christoph Meinel

In this paper, we propose an alternative evaluating metric for word analogy questions (A to B is as C to D) in word vector evaluation.

Conditional Adversarial Network for Semantic Segmentation of Brain Tumor

no code implementations17 Aug 2017 Mina Rezaei, Konstantin Harmuth, Willi Gierke, Thomas Kellermeier, Martin Fischer, Haojin Yang, Christoph Meinel

In this paper, we propose a novel end-to-end trainable architecture for brain tumor semantic segmentation through conditional adversarial training.

Computer Vision Semantic Segmentation

Brain Abnormality Detection by Deep Convolutional Neural Network

no code implementations17 Aug 2017 Mina Rezaei, Haojin Yang, Christoph Meinel

In this paper, we describe our method for classification of brain magnetic resonance (MR) images into different abnormalities and healthy classes based on the deep neural network.

Anomaly Detection Classification +1

Deep Learning for Medical Image Analysis

no code implementations17 Aug 2017 Mina Rezaei, Haojin Yang, Christoph Meinel

This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph. D. plan and several novels, end-to-end trainable approaches for analyzing medical images using deep learning algorithm.

Anomaly Detection

Deep Neural Network with l2-norm Unit for Brain Lesions Detection

no code implementations17 Aug 2017 Mina Rezaei, Haojin Yang, Christoph Meinel

Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations.

Language Identification Using Deep Convolutional Recurrent Neural Networks

1 code implementation16 Aug 2017 Christian Bartz, Tom Herold, Haojin Yang, Christoph Meinel

Language Identification (LID) systems are used to classify the spoken language from a given audio sample and are typically the first step for many spoken language processing tasks, such as Automatic Speech Recognition (ASR) systems.

Automatic Speech Recognition General Classification +3

STN-OCR: A single Neural Network for Text Detection and Text Recognition

3 code implementations27 Jul 2017 Christian Bartz, Haojin Yang, Christoph Meinel

In contrast to most existing works that consist of multiple deep neural networks and several pre-processing steps we propose to use a single deep neural network that learns to detect and recognize text from natural images in a semi-supervised way.

Optical Character Recognition Scene Text Detection +1

BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet

2 code implementations27 May 2017 Haojin Yang, Martin Fritzsche, Christian Bartz, Christoph Meinel

Binary Neural Networks (BNNs) can drastically reduce memory size and accesses by applying bit-wise operations instead of standard arithmetic operations.

Punctuation Prediction for Unsegmented Transcript Based on Word Vector

no code implementations LREC 2016 Xiaoyin Che, Cheng Wang, Haojin Yang, Christoph Meinel

In this paper we propose an approach to predict punctuation marks for unsegmented speech transcript.

Image Captioning with Deep Bidirectional LSTMs

1 code implementation4 Apr 2016 Cheng Wang, Haojin Yang, Christian Bartz, Christoph Meinel

This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning.

Data Augmentation Image Captioning +2

Cannot find the paper you are looking for? You can Submit a new open access paper.