380 citations · 1.6k across the 84 of their papers we have counts for
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Interpretation of NLP models through input marginalization
Siwon Kim, Jihun Yi, Eunji Kim +1
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…
Momentum Contrast Speaker Representation Learning
Jangho Lee, Jaihyun Koh, Sungroh Yoon
Unsupervised representation learning has shown remarkable achievement by reducing the performance gap with supervised feature learning, especially in the image domain. In this stud…
Information-Theoretic Visual Explanation for Black-Box Classifiers
Jihun Yi, Eunji Kim, Siwon Kim +1
In this work, we attempt to explain the prediction of any black-box classifier from an information-theoretic perspective. For each input feature, we compare the classifier outputs…
iCaps: An Interpretable Classifier via Disentangled Capsule Networks
Dahuin Jung, Jonghyun Lee, Jihun Yi +1
We propose an interpretable Capsule Network, iCaps, for image classification. A capsule is a group of neurons nested inside each layer, and the one in the last layer is called a cl…
Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
Jihun Yi, Sungroh Yoon
In this paper, we address the problem of image anomaly detection and segmentation. Anomaly detection involves making a binary decision as to whether an input image contains an anom…
Joint Contrastive Learning for Unsupervised Domain Adaptation
Changhwa Park, Jonghyun Lee, Jaeyoon Yoo +2
Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination. In par…
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
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. However, a flow…
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
Jaehyeon Kim, Sungwon Kim, Jungil Kong +1
Recently, text-to-speech (TTS) models such as FastSpeech and ParaNet have been proposed to generate mel-spectrograms from text in parallel. Despite the advantage, the parallel TTS…
T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
Seongsik Park, Seijoon Kim, Byunggook Na +1
Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their appli…
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
Saehyung Lee, Hyungyu Lee, Sungroh Yoon
Adversarial examples cause neural networks to produce incorrect outputs with high confidence. Although adversarial training is one of the most effective forms of defense against ad…