Search Results for author: Shuhui Qu

Found 7 papers, 3 papers with code

A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing

no code implementations ICLR 2019 Shuhui Qu, Janghwan Lee, Wei Xiong, Wonhyouk Jang, Jie Wang

Since the generated samples simulate the low density area for each modal, the discriminator could directly detect anomalies from normal data.

Anomaly Detection

End-to-end Quantized Training via Log-Barrier Extensions

no code implementations1 Jan 2021 Juncheng B Li, Shuhui Qu, Xinjian Li, Emma Strubell, Florian Metze

Quantization of neural network parameters and activations has emerged as a successful approach to reducing the model size and inference time on hardware that sup-ports native low-precision arithmetic.


Audio-Visual Event Recognition through the lens of Adversary

1 code implementation15 Nov 2020 Juncheng B Li, Kaixin Ma, Shuhui Qu, Po-Yao Huang, Florian Metze

This work aims to study several key questions related to multimodal learning through the lens of adversarial noises: 1) The trade-off between early/middle/late fusion affecting its robustness and accuracy 2) How do different frequency/time domain features contribute to the robustness?

Adversarial Music: Real World Audio Adversary Against Wake-word Detection System

no code implementations NeurIPS 2019 Juncheng B. Li, Shuhui Qu, Xinjian Li, Joseph Szurley, J. Zico Kolter, Florian Metze

In this work, we target our attack on the wake-word detection system, jamming the model with some inconspicuous background music to deactivate the VAs while our audio adversary is present.

Real-World Adversarial Attack

A Comparison of deep learning methods for environmental sound

1 code implementation20 Mar 2017 Juncheng Li, Wei Dai, Florian Metze, Shuhui Qu, Samarjit Das

On these features, we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and i-vector.

Learning Filter Banks Using Deep Learning For Acoustic Signals

no code implementations29 Nov 2016 Shuhui Qu, Juncheng Li, Wei Dai, Samarjit Das

Based on the procedure of log Mel-filter banks, we design a filter bank learning layer.

Very Deep Convolutional Neural Networks for Raw Waveforms

8 code implementations1 Oct 2016 Wei Dai, Chia Dai, Shuhui Qu, Juncheng Li, Samarjit Das

Our CNNs, with up to 34 weight layers, are efficient to optimize over very long sequences (e. g., vector of size 32000), necessary for processing acoustic waveforms.

Representation Learning

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