Search Results for author: Simyung Chang

Found 16 papers, 2 papers with code

Distribution Estimation to Automate Transformation Policies for Self-Supervision

no code implementations24 Nov 2021 Seunghan Yang, Debasmit Das, Simyung Chang, Sungrack Yun, Fatih Porikli

However, it is observed that image transformations already present in the dataset might be less effective in learning such self-supervised representations.

Self-Supervised Learning

Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization

no code implementations12 Nov 2021 Byeonggeun Kim, Seunghan Yang, Jangho Kim, Simyung Chang

Moreover, we introduce an efficient architecture, BC-ResNet-ASC, a modified version of the baseline architecture with a limited receptive field.

Acoustic Scene Classification Classification +5

Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation

no code implementations11 Nov 2021 John Yang, Yash Bhalgat, Simyung Chang, Fatih Porikli, Nojun Kwak

While hand pose estimation is a critical component of most interactive extended reality and gesture recognition systems, contemporary approaches are not optimized for computational and memory efficiency.

3D Hand Pose Estimation Gesture Recognition

Self-Evolutionary Optimization for Pareto Front Learning

no code implementations7 Oct 2021 Simyung Chang, KiYoon Yoo, Jiho Jang, Nojun Kwak

Utilizing SEO for PFL, we also introduce self-evolutionary Pareto networks (SEPNet), enabling the unified model to approximate the entire Pareto front set that maximizes the hypervolume.

Multi-Task Learning

Towards Robust Domain Generalization in 2D Neural Audio Processing

no code implementations29 Sep 2021 Byeonggeun Kim, Seunghan Yang, Jangho Kim, Hyunsin Park, Jun-Tae Lee, Simyung Chang

While using two-dimensional convolutional neural networks (2D-CNNs) in image processing, it is possible to manipulate domain information using channel statistics, and instance normalization has been a promising way to get domain-invariant features.

Acoustic Scene Classification Domain Generalization +3

PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation

no code implementations25 Jun 2021 Jangho Kim, Simyung Chang, Nojun Kwak

Unlike traditional pruning and KD, PQK makes use of unimportant weights pruned in the pruning process to make a teacher network for training a better student network without pre-training the teacher model.

Keyword Spotting Knowledge Distillation +2

Broadcasted Residual Learning for Efficient Keyword Spotting

2 code implementations8 Jun 2021 Byeonggeun Kim, Simyung Chang, Jinkyu Lee, Dooyong Sung

We present a broadcasted residual learning method to achieve high accuracy with small model size and computational load.

Keyword Spotting

SubSpectral Normalization for Neural Audio Data Processing

no code implementations25 Mar 2021 Simyung Chang, Hyoungwoo Park, Janghoon Cho, Hyunsin Park, Sungrack Yun, Kyuwoong Hwang

In this work, we introduce SubSpectral Normalization (SSN), which splits the input frequency dimension into several groups (sub-bands) and performs a different normalization for each group.

 Ranked #1 on Keyword Spotting on Google Speech Commands (% Test Accuracy metric)

Affine Transformation Keyword Spotting

Prototype-based Personalized Pruning

no code implementations25 Mar 2021 Jangho Kim, Simyung Chang, Sungrack Yun, Nojun Kwak

We verify the usefulness of PPP on a couple of tasks in computer vision and Keyword spotting.

Keyword Spotting Model Compression

URNet : User-Resizable Residual Networks with Conditional Gating Module

no code implementations15 Jan 2019 Sang-ho Lee, Simyung Chang, Nojun Kwak

There are methods to reduce the cost by compressing networks or varying its computational path dynamically according to the input image.

Genetic-Gated Networks for Deep Reinforcement Learning

no code implementations NeurIPS 2018 Simyung Chang, John Yang, Jaeseok Choi, Nojun Kwak

We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks.

Sym-parameterized Dynamic Inference for Mixed-Domain Image Translation

1 code implementation ICCV 2019 Simyung Chang, SeongUk Park, John Yang, Nojun Kwak

Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network.

Image-to-Image Translation Translation

Genetic-Gated Networks for Deep Reinforcement

no code implementations26 Nov 2018 Simyung Chang, John Yang, Jae-Seok Choi, Nojun Kwak

We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks.

Towards Governing Agent's Efficacy: Action-Conditional $β$-VAE for Deep Transparent Reinforcement Learning

no code implementations11 Nov 2018 John Yang, Gyujeong Lee, Minsung Hyun, Simyung Chang, Nojun Kwak

We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way.

Representation Learning

Broadcasting Convolutional Network for Visual Relational Reasoning

no code implementations ECCV 2018 Simyung Chang, John Yang, SeongUk Park, Nojun Kwak

In this paper, we propose the Broadcasting Convolutional Network (BCN) that extracts key object features from the global field of an entire input image and recognizes their relationship with local features.

Relational Reasoning

BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning

no code implementations5 Sep 2017 Simyung Chang, Youngjoon Yoo, Jae-Seok Choi, Nojun Kwak

Our method learns hundreds to thousand times faster than the conventional methods by learning only a handful of core cluster information, which shows that deep RL agents can effectively learn through the shared knowledge from other agents.

Imitation Learning

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