Search Results for author: Haojin Yang

Found 40 papers, 18 papers with code

ImbaGCD: Imbalanced Generalized Category Discovery

no code implementations4 Dec 2023 Ziyun Li, Ben Dai, Furkan Simsek, Christoph Meinel, Haojin Yang

Therefore, we present a challenging and practical problem, Imbalanced Generalized Category Discovery (ImbaGCD), where the distribution of unlabeled data is imbalanced, with known classes being more frequent than unknown ones.

Generalized Categories Discovery for Long-tailed Recognition

no code implementations4 Dec 2023 Ziyun Li, Christoph Meinel, Haojin Yang

Generalized Class Discovery (GCD) plays a pivotal role in discerning both known and unknown categories from unlabeled datasets by harnessing the insights derived from a labeled set comprising recognized classes.

Scaled Prompt-Tuning for Few-Shot Natural Language Generation

no code implementations13 Sep 2023 Ting Hu, Christoph Meinel, Haojin Yang

The increasingly Large Language Models (LLMs) demonstrate stronger language understanding and generation capabilities, while the memory demand and computation cost of fine-tuning LLMs on downstream tasks are non-negligible.

Text Generation

Supervised Knowledge May Hurt Novel Class Discovery Performance

1 code implementation6 Jun 2023 Ziyun Li, Jona Otholt, Ben Dai, Di Hu, Christoph Meinel, Haojin Yang

Next, by using the proposed transfer flow, we conduct various empirical experiments with different levels of semantic similarity, yielding that supervised knowledge may hurt NCD performance.

Novel Class Discovery Semantic Similarity +1

Join the High Accuracy Club on ImageNet with A Binary Neural Network Ticket

1 code implementation23 Nov 2022 Nianhui Guo, Joseph Bethge, Christoph Meinel, Haojin Yang

In this work, we revisit the potential of binary neural networks and focus on a compelling but unanswered problem: how can a binary neural network achieve the crucial accuracy level (e. g., 80%) on ILSVRC-2012 ImageNet?

Data Augmentation Knowledge Distillation +1

Empirical Evaluation of Post-Training Quantization Methods for Language Tasks

no code implementations29 Oct 2022 Ting Hu, Christoph Meinel, Haojin Yang

We further explore the limit of quantization bit and show that OCS could quantize BERT-Base and BERT-Large to 3-bits and retain 98% and 96% of the performance on the GLUE benchmark accordingly.

Attribute Quantization +1

A Closer Look at Novel Class Discovery from the Labeled Set

no code implementations19 Sep 2022 Ziyun Li, Jona Otholt, Ben Dai, Di Hu, Christoph Meinel, Haojin Yang

Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes.

Novel Class Discovery Semantic Similarity +1

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.

AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks

1 code implementation15 Apr 2021 Haojin Yang, Zhen Shen, Yucheng Zhao

Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications.

Image Classification

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.


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

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.


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.

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.


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.

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 +5

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 object-detection +1

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 +3

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.

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.

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 +1

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.

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

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.

Generative Adversarial Network Segmentation +2

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 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 Automatic Speech Recognition (ASR) +4

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 (OCR) Scene Text Detection +2

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

3 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.

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 +4

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