Search Results for author: Sungroh Yoon

Found 113 papers, 42 papers with code

ControlDreamer: Stylized 3D Generation with Multi-View ControlNet

no code implementations2 Dec 2023 Yeongtak Oh, Jooyoung Choi, Yongsung Kim, MinJun Park, Chaehun Shin, Sungroh Yoon

Recent advancements in text-to-3D generation have significantly contributed to the automation and democratization of 3D content creation.

text-guided-generation Text to 3D

On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model

no code implementations14 Nov 2023 Nohil Park, Joonsuk Park, Kang Min Yoo, Sungroh Yoon

An exciting advancement in the field of multilingual models is the emergence of autoregressive models with zero- and few-shot capabilities, a phenomenon widely reported in large-scale language models.


Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor

no code implementations13 Nov 2023 Sangwon Yu, Changmin Lee, Hojin Lee, Sungroh Yoon

We employ ScoPE to facilitate text generation in the target domain by integrating it with language models through a cascading approach.

Language Modelling Text Generation

1.5 million materials narratives generated by chatbots

no code implementations25 Aug 2023 Yang Jeong Park, Sung Eun Jerng, Jin-Sung Park, Choah Kwon, Chia-Wei Hsu, Zhichu Ren, Sungroh Yoon, Ju Li

The advent of artificial intelligence (AI) has enabled a comprehensive exploration of materials for various applications.

Language Modelling Large Language Model

Mass Spectra Prediction with Structural Motif-based Graph Neural Networks

no code implementations28 Jun 2023 Jiwon Park, Jeonghee Jo, Sungroh Yoon

Mass spectra, which are agglomerations of ionized fragments from targeted molecules, play a crucial role across various fields for the identification of molecular structures.

Improving Visual Prompt Tuning for Self-supervised Vision Transformers

1 code implementation8 Jun 2023 Seungryong Yoo, Eunji Kim, Dahuin Jung, Jungbeom Lee, Sungroh Yoon

Visual Prompt Tuning (VPT) is an effective tuning method for adapting pretrained Vision Transformers (ViTs) to downstream tasks.

Image Classification Semantic Segmentation

Probabilistic Concept Bottleneck Models

1 code implementation2 Jun 2023 Eunji Kim, Dahuin Jung, Sangha Park, Siwon Kim, Sungroh Yoon

To provide a reliable interpretation against this ambiguity, we propose Probabilistic Concept Bottleneck Models (ProbCBM).

Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection

no code implementations30 May 2023 Daegyu Kim, Chaehun Shin, Jooyoung Choi, Dahuin Jung, Sungroh Yoon

Diffusion-Stego achieved a high capacity of messages (3. 0 bpp of binary messages with 98% accuracy, and 6. 0 bpp with 90% accuracy) as well as high quality (with a FID score of 2. 77 for 1. 0 bpp on the FFHQ 64$\times$64 dataset) that makes it challenging to distinguish from real images in the PNG format.

Denoising Image Generation

On the Impact of Knowledge Distillation for Model Interpretability

no code implementations25 May 2023 Hyeongrok Han, Siwon Kim, Hyun-Soo Choi, Sungroh Yoon

Several recent studies have elucidated why knowledge distillation (KD) improves model performance.

Knowledge Distillation

Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models

no code implementations25 May 2023 Jooyoung Choi, Yunjey Choi, Yunji Kim, Junho Kim, Sungroh Yoon

Text-to-image diffusion models can generate diverse, high-fidelity images based on user-provided text prompts.


Edit-A-Video: Single Video Editing with Object-Aware Consistency

no code implementations14 Mar 2023 Chaehun Shin, Heeseung Kim, Che Hyun Lee, Sang-gil Lee, Sungroh Yoon

Despite the fact that text-to-video (TTV) model has recently achieved remarkable success, there have been few approaches on TTV for its extension to video editing.

Video Editing

Sample-efficient Adversarial Imitation Learning

no code implementations14 Mar 2023 Dahuin Jung, Hyungyu Lee, Sungroh Yoon

In particular, in comparison with existing self-supervised learning methods for tabular data, we propose a different corruption method for state and action representations that is robust to diverse distortions.

Imitation Learning Representation Learning +1

New Insights for the Stability-Plasticity Dilemma in Online Continual Learning

1 code implementation17 Feb 2023 Dahuin Jung, Dongjin Lee, Sunwon Hong, Hyemi Jang, Ho Bae, Sungroh Yoon

The aim of continual learning is to learn new tasks continuously (i. e., plasticity) without forgetting previously learned knowledge from old tasks (i. e., stability).

Continual Learning

FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks

1 code implementation25 Oct 2022 Jaehee Jang, Heonseok Ha, Dahuin Jung, Sungroh Yoon

While the existing methods require the collection of auxiliary data or model weights to generate a counterpart, FedClassAvg only requires clients to communicate with a couple of fully connected layers, which is highly communication-efficient.

Personalized Federated Learning Representation Learning +1

Confidence Score for Source-Free Unsupervised Domain Adaptation

1 code implementation14 Jun 2022 Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon

Unlike existing confidence scores that use only one of the source or target domain knowledge, the JMDS score uses both knowledge.

Unsupervised Domain Adaptation

Out of Sight, Out of Mind: A Source-View-Wise Feature Aggregation for Multi-View Image-Based Rendering

no code implementations10 Jun 2022 Geonho Cha, Chaehun Shin, Sungroh Yoon, Dongyoon Wee

Finally, for each element in the feature set, the aggregation features are extracted by calculating the weighted means and variances, where the weights are derived from the similarity distributions.

BigVGAN: A Universal Neural Vocoder with Large-Scale Training

3 code implementations9 Jun 2022 Sang-gil Lee, Wei Ping, Boris Ginsburg, Bryan Catanzaro, Sungroh Yoon

Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments.

Audio Generation Inductive Bias +2

Guided-TTS 2: A Diffusion Model for High-quality Adaptive Text-to-Speech with Untranscribed Data

no code implementations30 May 2022 Sungwon Kim, Heeseung Kim, Sungroh Yoon

We train the speaker-conditional diffusion model on large-scale untranscribed datasets for a classifier-free guidance method and further fine-tune the diffusion model on the reference speech of the target speaker for adaptation, which only takes 40 seconds.

Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization

no code implementations11 Apr 2022 Jungbeom Lee, Eunji Kim, Jisoo Mok, Sungroh Yoon

This manipulation is realized in an anti-adversarial manner, so that the original image is perturbed along pixel gradients in directions opposite to those used in an adversarial attack.

Adversarial Attack Semi-Supervised Semantic Segmentation +3

Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization

no code implementations CVPR 2022 Eunji Kim, Siwon Kim, Jungbeom Lee, Hyunwoo Kim, Sungroh Yoon

Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels.

Weakly-Supervised Object Localization

Perception Prioritized Training of Diffusion Models

5 code implementations CVPR 2022 Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, Sungroh Yoon

Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i. e., denoising score matching loss.


Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?

1 code implementation CVPR 2022 Jisoo Mok, Byunggook Na, Ji-Hoon Kim, Dongyoon Han, Sungroh Yoon

To take such non-linear characteristics into account, we introduce Label-Gradient Alignment (LGA), a novel NTK-based metric whose inherent formulation allows it to capture the large amount of non-linear advantage present in modern neural architectures.

Neural Architecture Search

Weakly Supervised Semantic Segmentation using Out-of-Distribution Data

1 code implementation CVPR 2022 Jungbeom Lee, Seong Joon Oh, Sangdoo Yun, Junsuk Choe, Eunji Kim, Sungroh Yoon

However, training on class labels only, classifiers suffer from the spurious correlation between foreground and background cues (e. g. train and rail), fundamentally bounding the performance of WSSS.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Dataset Condensation with Contrastive Signals

2 code implementations7 Feb 2022 Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon

However, in this study, we prove that the existing DC methods can perform worse than the random selection method when task-irrelevant information forms a significant part of the training dataset.

Continual Learning Dataset Condensation +1

AutoSNN: Towards Energy-Efficient Spiking Neural Networks

1 code implementation30 Jan 2022 Byunggook Na, Jisoo Mok, Seongsik Park, Dongjin Lee, Hyeokjun Choe, Sungroh Yoon

We investigate the design choices used in the previous studies in terms of the accuracy and number of spikes and figure out that they are not best-suited for SNNs.

Neural Architecture Search

Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images

2 code implementations2 Dec 2021 Sang-gil Lee, Eunji Kim, Jae Seok Bae, Jung Hoon Kim, Sungroh Yoon

The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis.

Automatic Liver And Tumor Segmentation Computed Tomography (CT) +4

Supervised Neural Discrete Universal Denoiser for Adaptive Denoising

no code implementations24 Nov 2021 Sungmin Cha, Seonwoo Min, Sungroh Yoon, Taesup Moon

Namely, we make the supervised pre-training of Neural DUDE compatible with the adaptive fine-tuning of the parameters based on the given noisy data subject to denoising.


Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance

no code implementations23 Nov 2021 Heeseung Kim, Sungwon Kim, Sungroh Yoon

For TTS synthesis, we guide the generative process of the diffusion model with a phoneme classifier trained on a large-scale speech recognition dataset.

speech-recognition Speech Recognition +2

Scalable Smartphone Cluster for Deep Learning

no code implementations23 Oct 2021 Byunggook Na, Jaehee Jang, Seongsik Park, Seijoon Kim, Joonoo Kim, Moon Sik Jeong, Kwang Choon Kim, Seon Heo, Yoonsang Kim, Sungroh Yoon

We implemented large-batch synchronous training of DNNs based on Caffe, a deep learning library.

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation

1 code implementation NeurIPS 2021 Jungbeom Lee, Jooyoung Choi, Jisoo Mok, Sungroh Yoon

Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

FICGAN: Facial Identity Controllable GAN for De-identification

no code implementations2 Oct 2021 Yonghyun Jeong, Jooyoung Choi, Sungwon Kim, Youngmin Ro, Tae-Hyun Oh, Doyeon Kim, Heonseok Ha, Sungroh Yoon

In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility.


Variational Perturbations for Visual Feature Attribution

no code implementations29 Sep 2021 Jae Myung Kim, Eunji Kim, Sungroh Yoon, Jungwoo Lee, Cordelia Schmid, Zeynep Akata

Explaining a complex black-box system in a post-hoc manner is important to understand its predictions.

Biased Multi-Domain Adversarial Training

no code implementations29 Sep 2021 Saehyung Lee, Hyungyu Lee, Sanghyuk Chun, Sungroh Yoon

Several recent studies have shown that the use of extra in-distribution data can lead to a high level of adversarial robustness.

Adversarial Robustness

Guided-TTS:Text-to-Speech with Untranscribed Speech

no code implementations29 Sep 2021 Heeseung Kim, Sungwon Kim, Sungroh Yoon

By modeling the unconditional distribution for speech, our model can utilize the untranscribed data for training.

Speech Synthesis Text-To-Speech Synthesis

Confidence Score Weighting Adaptation for Source-Free Unsupervised Domain Adaptation

no code implementations29 Sep 2021 Jonghyun Lee, Dahuin Jung, Junho Yim, Sungroh Yoon

Unsupervised domain adaptation (UDA) aims to achieve high performance within the unlabeled target domain by leveraging the labeled source domain.

Pseudo Label Unsupervised Domain Adaptation

AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate

no code implementations EMNLP 2021 Jongyoon Song, Sungwon Kim, Sungroh Yoon

Non-autoregressive neural machine translation (NART) models suffer from the multi-modality problem which causes translation inconsistency such as token repetition.

Knowledge Distillation Machine Translation +1

Towards a Rigorous Evaluation of Time-series Anomaly Detection

1 code implementation11 Sep 2021 Siwon Kim, Kukjin Choi, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon

Furthermore, we question the potential of existing TAD methods by showing that an untrained model obtains comparable detection performance to the existing methods even when PA is forbidden.

Anomaly Detection Time Series +1

ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

1 code implementation ICCV 2021 Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, Sungroh Yoon

In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image.

Denoising Image Generation +2

AdvRush: Searching for Adversarially Robust Neural Architectures

1 code implementation ICCV 2021 Jisoo Mok, Byunggook Na, Hyeokjun Choe, Sungroh Yoon

Current efforts to improve the robustness of neural networks against adversarial examples are focused on developing robust training methods, which update the weights of a neural network in a more robust direction.

Adversarial Robustness Neural Architecture Search

Toward Spatially Unbiased Generative Models

2 code implementations ICCV 2021 Jooyoung Choi, Jungbeom Lee, Yonghyun Jeong, Sungroh Yoon

From our observations, the generator's implicit positional encoding is translation-variant, making the generator spatially biased.

Denoising Image Generation +1

TargetNet: Functional microRNA Target Prediction with Deep Neural Networks

1 code implementation23 Jul 2021 Seonwoo Min, Byunghan Lee, Sungroh Yoon

Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction.

Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation

no code implementations14 Jun 2021 Jeonghee Jo, Bumju Kwak, Byunghan Lee, Sungroh Yoon

Message passing neural network provides an effective framework for capturing molecular geometric features with the perspective of a molecule as a graph.

Molecular Property Prediction Property Prediction

Energy-efficient Knowledge Distillation for Spiking Neural Networks

no code implementations14 Jun 2021 Dongjin Lee, Seongsik Park, Jongwan Kim, Wuhyeong Doh, Sungroh Yoon

On MNIST dataset, our proposed student SNN achieves up to 0. 09% higher accuracy and produces 65% less spikes compared to the student SNN trained with conventional knowledge distillation method.

Knowledge Distillation Model Compression +1

Accelerating Neural Architecture Search via Proxy Data

1 code implementation9 Jun 2021 Byunggook Na, Jisoo Mok, Hyeokjun Choe, Sungroh Yoon

By analyzing proxy data constructed using various selection methods through data entropy, we propose a novel proxy data selection method tailored for NAS.

Neural Architecture Search

Stein Latent Optimization for Generative Adversarial Networks

1 code implementation ICLR 2022 Uiwon Hwang, Heeseung Kim, Dahuin Jung, Hyemi Jang, Hyungyu Lee, Sungroh Yoon

Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner.

Training Energy-Efficient Deep Spiking Neural Networks with Time-to-First-Spike Coding

no code implementations4 Jun 2021 Seongsik Park, Sungroh Yoon

With TTFS coding, each neuron generates one spike at most, which leads to a significant improvement in energy efficiency.

Noise-Robust Deep Spiking Neural Networks with Temporal Information

no code implementations22 Apr 2021 Seongsik Park, Dongjin Lee, Sungroh Yoon

Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information.

XProtoNet: Diagnosis in Chest Radiography with Global and Local Explanations

1 code implementation CVPR 2021 Eunji Kim, Siwon Kim, Minji Seo, Sungroh Yoon

Automated diagnosis using deep neural networks in chest radiography can help radiologists detect life-threatening diseases.

Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation

1 code implementation CVPR 2021 Jungbeom Lee, Eunji Kim, Sungroh Yoon

Weakly supervised semantic segmentation produces a pixel-level localization from a classifier, but it is likely to restrict its focus to a small discriminative region of the target object.

Adversarial Attack Segmentation +3

Removing Undesirable Feature Contributions Using Out-of-Distribution Data

1 code implementation ICLR 2021 Saehyung Lee, Changhwa Park, Hyungyu Lee, Jihun Yi, Jonghyun Lee, Sungroh Yoon

Herein, we propose a data augmentation method to improve generalization in both adversarial and standard learning by using out-of-distribution (OOD) data that are devoid of the abovementioned issues.

Data Augmentation

Variational saliency maps for explaining model's behavior

no code implementations1 Jan 2021 Jae Myung Kim, Eunji Kim, Seokhyeon Ha, Sungroh Yoon, Jungwoo Lee

Saliency maps have been widely used to explain the behavior of an image classifier.

Interpretation of NLP models through input marginalization

no code implementations EMNLP 2020 Siwon Kim, Jihun Yi, Eunji Kim, Sungroh Yoon

To demystify the "black box" property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an input.

Natural Language Inference Sentiment Analysis

Information-Theoretic Visual Explanation for Black-Box Classifiers

1 code implementation23 Sep 2020 Jihun Yi, Eunji Kim, Siwon Kim, Sungroh Yoon

IG map provides a class-independent answer to "How informative is each pixel?

Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation

3 code implementations29 Jun 2020 Jihun Yi, Sungroh Yoon

In this paper, we address the problem of image anomaly detection and segmentation.

Ranked #7 on Anomaly Detection on BTAD (using extra training data)

Anomaly Detection Segmentation +1

Joint Contrastive Learning for Unsupervised Domain Adaptation

1 code implementation18 Jun 2020 Changhwa Park, Jonghyun Lee, Jaeyoon Yoo, Minhoe Hur, Sungroh Yoon

Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination.

Contrastive Learning Unsupervised Domain Adaptation

NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity

1 code implementation NeurIPS 2020 Sang-gil Lee, Sungwon Kim, Sungroh Yoon

Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis.

Density Estimation Normalising Flows +1

Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search

5 code implementations NeurIPS 2020 Jaehyeon Kim, Sungwon Kim, Jungil Kong, Sungroh Yoon

By leveraging the properties of flows, MAS searches for the most probable monotonic alignment between text and the latent representation of speech.

Ranked #4 on Text-To-Speech Synthesis on LJSpeech (using extra training data)

Text-To-Speech Synthesis

T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding

no code implementations26 Mar 2020 Seongsik Park, Seijoon Kim, Byunggook Na, Sungroh Yoon

Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their applicability in practical machine learning problems.

Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization

2 code implementations CVPR 2020 Saehyung Lee, Hyungyu Lee, Sungroh Yoon

In this paper, we identify Adversarial Feature Overfitting (AFO), which may cause poor adversarially robust generalization, and we show that adversarial training can overshoot the optimal point in terms of robust generalization, leading to AFO in our simple Gaussian model.

Adversarial Robustness Data Augmentation +1

Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information

1 code implementation25 Nov 2019 Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon

Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling.

Language Modelling Masked Language Modeling

Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation

no code implementations ICCV 2019 Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, Sungroh Yoon

We propose a method of using videos automatically harvested from the web to identify a larger region of the target object by using temporal information, which is not present in the static image.

Optical Flow Estimation Test +2

One-Shot Learning for Text-to-SQL Generation

no code implementations26 Apr 2019 Dongjun Lee, Jaesik Yoon, Jongyun Song, Sang-gil Lee, Sungroh Yoon

We show that our model outperforms state-of-the-art approaches for various text-to-SQL datasets in two aspects: 1) the SQL generation accuracy for the trained templates, and 2) the adaptability to the unseen SQL templates based on a single example without any additional training.

One-Shot Learning Text-To-SQL

Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection

no code implementations12 Mar 2019 Seijoon Kim, Seongsik Park, Byunggook Na, Sungroh Yoon

Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications.

Image Classification object-detection +1

Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation

no code implementations12 Mar 2019 Jaeyoon Yoo, Changhwa Park, Yongjun Hong, Sungroh Yoon

We propose a novel domain adaptation method based on label propagation and cycle consistency to let the clusters of the features from the two domains overlap exactly and become clear for high accuracy.

Unsupervised Domain Adaptation

PuVAE: A Variational Autoencoder to Purify Adversarial Examples

no code implementations2 Mar 2019 Uiwon Hwang, Jaewoo Park, Hyemi Jang, Sungroh Yoon, Nam Ik Cho

Deep neural networks are widely used and exhibit excellent performance in many areas.

PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation

no code implementations28 Feb 2019 Dahuin Jung, Ho Bae, Hyun-Soo Choi, Sungroh Yoon

We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images.


FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference

no code implementations CVPR 2019 Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, Sungroh Yoon

The main obstacle to weakly supervised semantic image segmentation is the difficulty of obtaining pixel-level information from coarse image-level annotations.

Image Classification Image Segmentation +1

HexaGAN: Generative Adversarial Nets for Real World Classification

1 code implementation26 Feb 2019 Uiwon Hwang, Dahuin Jung, Sungroh Yoon

We evaluate the classification performance (F1-score) of the proposed method with 20% missingness and confirm up to a 5% improvement in comparison with the performance of combinations of state-of-the-art methods.

Classification General Classification +1

AnomiGAN: Generative adversarial networks for anonymizing private medical data

no code implementations31 Jan 2019 Ho Bae, Dahuin Jung, Sungroh Yoon

We compared our method to state-of-the-art techniques and observed that our method preserves the same level of privacy as differential privacy (DP), but had better prediction results.

FloWaveNet : A Generative Flow for Raw Audio

2 code implementations6 Nov 2018 Sungwon Kim, Sang-gil Lee, Jongyoon Song, Sungroh Yoon

Most of modern text-to-speech architectures use a WaveNet vocoder for synthesizing a high-fidelity waveform audio, but there has been a limitation for practical applications due to its slow autoregressive sampling scheme.

Sound Audio and Speech Processing

Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks

no code implementations10 Sep 2018 Seongsik Park, Seijoon Kim, Hyeokjun Choe, Sungroh Yoon

The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability.

Image Classification

Security and Privacy Issues in Deep Learning

no code implementations31 Jul 2018 Ho Bae, Jaehee Jang, Dahuin Jung, Hyemi Jang, Heonseok Ha, Hyungyu Lee, Sungroh Yoon

Furthermore, the privacy of the data involved in model training is also threatened by attacks such as the model-inversion attack, or by dishonest service providers of AI applications.

Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector

1 code implementation2 Jul 2018 Sang-gil Lee, Jae Seok Bae, Hyunjae Kim, Jung Hoon Kim, Sungroh Yoon

We present a focal liver lesion detection model leveraged by custom-designed multi-phase computed tomography (CT) volumes, which reflects real-world clinical lesion detection practice using a Single Shot MultiBox Detector (SSD).

Computed Tomography (CT) Lesion Detection +2

Deep Trustworthy Knowledge Tracing

no code implementations28 May 2018 Heonseok Ha, Uiwon Hwang, Yongjun Hong, Jahee Jang, Sungroh Yoon

Knowledge tracing (KT), a key component of an intelligent tutoring system, is a machine learning technique that estimates the mastery level of a student based on his/her past performance.

Knowledge Tracing

Mutual Suppression Network for Video Prediction using Disentangled Features

no code implementations13 Apr 2018 Jungbeom Lee, Jangho Lee, Sungmin Lee, Sungroh Yoon

Video prediction can be performed by finding features in recent frames, and using them to generate approximations to upcoming frames.

 Ranked #1 on Video Prediction on KTH (Cond metric)

Optical Flow Estimation Representation Learning +1

Domain Adaptation Using Adversarial Learning for Autonomous Navigation

no code implementations11 Dec 2017 Jaeyoon Yoo, Yongjun Hong, Yung-Kyun Noh, Sungroh Yoon

The objective of this study is to train an autonomous navigation model that uses a simulator (instead of real labeled data) and an inexpensive monocular camera.

Autonomous Navigation Domain Adaptation +1

Deep Recurrent Neural Network-Based Identification of Precursor microRNAs

1 code implementation NeurIPS 2017 Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon

MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) which play key roles in post-transcriptional gene regulation.

Homomorphic Parameter Compression for Distributed Deep Learning Training

no code implementations28 Nov 2017 Jaehee Jang, Byungook Na, Sungroh Yoon

Distributed training of deep neural networks has received significant research interest, and its major approaches include implementations on multiple GPUs and clusters.

How Generative Adversarial Networks and Their Variants Work: An Overview

no code implementations16 Nov 2017 Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, Sungroh Yoon

Generative Adversarial Networks (GAN) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution.

Domain Adaptation Image Generation +1

Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data

no code implementations11 Nov 2017 Uiwon Hwang, Sungwoon Choi, Han-Byoel Lee, Sungroh Yoon

Electronic health records (EHRs) have contributed to the computerization of patient records and can thus be used not only for efficient and systematic medical services, but also for research on biomedical data science.

Disease Prediction Imputation +1

Quantized Memory-Augmented Neural Networks

no code implementations10 Nov 2017 Seongsik Park, Seijoon Kim, Seil Lee, Ho Bae, Sungroh Yoon

In this paper, we identify memory addressing (specifically, content-based addressing) as the main reason for the performance degradation and propose a robust quantization method for MANNs to address the challenge.


Polyphonic Music Generation with Sequence Generative Adversarial Networks

1 code implementation31 Oct 2017 Sang-gil Lee, Uiwon Hwang, Seonwoo Min, Sungroh Yoon

We propose an application of sequence generative adversarial networks (SeqGAN), which are generative adversarial networks for discrete sequence generation, for creating polyphonic musical sequences.

Sound Audio and Speech Processing

Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning

no code implementations28 Jun 2017 Jaeyoon Yoo, Heonseok Ha, Jihun Yi, Jongha Ryu, Chanju Kim, Jung-Woo Ha, Young-Han Kim, Sungroh Yoon

Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user.

Imitation Learning Recommendation Systems +2

DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction

2 code implementations27 Apr 2017 Sunyoung Kwon, Sungroh Yoon

To guarantee the commutative property for homogeneous interaction, we apply model sharing and hidden representation merging techniques.

Feature Engineering

DNA Steganalysis Using Deep Recurrent Neural Networks

no code implementations27 Apr 2017 Ho Bae, Byunghan Lee, Sunyoung Kwon, Sungroh Yoon

We compare our proposed method to various existing methods and biological sequence analysis methods implemented on top of our framework.


Building a Neural Machine Translation System Using Only Synthetic Parallel Data

no code implementations2 Apr 2017 Jae-hong Park, Jongyoon Song, Sungroh Yoon

Experiments on Czech-German and French-German translations demonstrate the efficacy of the proposed pseudo parallel corpus, which shows not only enhanced results for bidirectional translation tasks but also substantial improvement with the aid of a ground truth real parallel corpus.

Machine Translation NMT +1

Training IBM Watson using Automatically Generated Question-Answer Pairs

no code implementations12 Nov 2016 Jangho Lee, Gyuwan Kim, Jaeyoon Yoo, Changwoo Jung, Minseok Kim, Sungroh Yoon

Under the assumption that using such an automatically generated dataset could relieve the burden of manual question-answer generation, we tried to use this dataset to train an instance of Watson and checked the training efficiency and accuracy.

Answer Generation Question-Answer-Generation +1

An Efficient Approach to Boosting Performance of Deep Spiking Network Training

no code implementations8 Nov 2016 Seongsik Park, Sang-gil Lee, Hyunha Nam, Sungroh Yoon

In order to eliminate this workaround, recently proposed is a new class of SNN named deep spiking networks (DSNs), which can be trained directly (without a mapping from conventional deep networks) by error backpropagation with stochastic gradient descent.

Near-Data Processing for Differentiable Machine Learning Models

no code implementations6 Oct 2016 Hyeokjun Choe, Seil Lee, Hyunha Nam, Seongsik Park, Seijoon Kim, Eui-Young Chung, Sungroh Yoon

The second is the popularity of NAND flash-based solid-state drives (SSDs) containing multicore processors that can accommodate extra computation for data processing.

BIG-bench Machine Learning

Neural Universal Discrete Denoiser

no code implementations NeurIPS 2016 Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon

We present a new framework of applying deep neural networks (DNN) to devise a universal discrete denoiser.


deepMiRGene: Deep Neural Network based Precursor microRNA Prediction

no code implementations29 Apr 2016 Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon

Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology.

Feature Engineering

deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks

1 code implementation30 Mar 2016 Byunghan Lee, Junghwan Baek, Seunghyun Park, Sungroh Yoon

MicroRNAs (miRNAs) are short sequences of ribonucleic acids that control the expression of target messenger RNAs (mRNAs) by binding them.

Deep Learning in Bioinformatics

no code implementations21 Mar 2016 Seonwoo Min, Byunghan Lee, Sungroh Yoon

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics.

DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters

no code implementations26 Feb 2016 Hanjoo Kim, Jae-hong Park, Jaehee Jang, Sungroh Yoon

The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training.

Distributed Computing

DNA-Level Splice Junction Prediction using Deep Recurrent Neural Networks

no code implementations16 Dec 2015 Byunghan Lee, Taehoon Lee, Byunggook Na, Sungroh Yoon

A eukaryotic gene consists of multiple exons (protein coding regions) and introns (non-coding regions), and a splice junction refers to the boundary between a pair of exon and intron.


Manifold Regularized Deep Neural Networks using Adversarial Examples

no code implementations19 Nov 2015 Taehoon Lee, Minsuk Choi, Sungroh Yoon

Learning meaningful representations using deep neural networks involves designing efficient training schemes and well-structured networks.

General Classification

NASCUP: Nucleic Acid Sequence Classification by Universal Probability

1 code implementation16 Nov 2015 Sunyoung Kwon, Gyuwan Kim, Byunghan Lee, Jongsik Chun, Sungroh Yoon, Young-Han Kim

Motivated by the need for fast and accurate classification of unlabeled nucleotide sequences on a large scale, we developed NASCUP, a new classification method that captures statistical structures of nucleotide sequences by compact context-tree models and universal probability from information theory.

Genomics Information Theory Information Theory

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