Search Results for author: Taegyun Jeon

Found 10 papers, 3 papers with code

Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector

no code implementations4 Oct 2019 Junghoon Seo, Seungwon Lee, Beomsu Kim, Taegyun Jeon

In this paper, we revisit the classical bootstrap aggregating approach in the context of modern transfer learning for data-efficient disaster damage detection.

Ensemble Learning imbalanced classification +1

Deep Closed-Form Subspace Clustering

no code implementations26 Aug 2019 Junghoon Seo, Jamyoung Koo, Taegyun Jeon

We propose Deep Closed-Form Subspace Clustering (DCFSC), a new embarrassingly simple model for subspace clustering with learning non-linear mapping.

NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations

no code implementations22 Aug 2019 Yooseung Wang, Junghoon Seo, Taegyun Jeon

Road extraction from very high resolution satellite (VHR) images is one of the most important topics in the field of remote sensing.

Bridging Adversarial Robustness and Gradient Interpretability

1 code implementation27 Mar 2019 Beomsu Kim, Junghoon Seo, Taegyun Jeon

Adversarial training is a training scheme designed to counter adversarial attacks by augmenting the training dataset with adversarial examples.

Adversarial Robustness

Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps

2 code implementations13 Feb 2019 Beomsu Kim, Junghoon Seo, SeungHyun Jeon, Jamyoung Koo, Jeongyeol Choe, Taegyun Jeon

Saliency Map, the gradient of the score function with respect to the input, is the most basic technique for interpreting deep neural network decisions.

Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative

no code implementations8 Jun 2018 Junghoon Seo, Jeongyeol Choe, Jamyoung Koo, Seunghyeon Jeon, Beomsu Kim, Taegyun Jeon

SmoothGrad and VarGrad are techniques that enhance the empirical quality of standard saliency maps by adding noise to input.

Domain Adaptive Generation of Aircraft on Satellite Imagery via Simulated and Unsupervised Learning

no code implementations8 Jun 2018 Junghoon Seo, Seunghyun Jeon, Taegyun Jeon

Object detection and classification for aircraft are the most important tasks in the satellite image analysis.

Classification General Classification +1

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