Search Results for author: Yu-Ting Chen

Found 16 papers, 3 papers with code

Context-Aware Temperature for Language Modeling

no code implementations1 Jan 2021 Pei-Hsin Wang, Sheng-Iou Hsieh, Shih-Chieh Chang, Yu-Ting Chen, Da-Cheng Juan, Jia-Yu Pan, Wei Wei

Current practices to apply temperature scaling assume either a fixed, or a manually-crafted dynamically changing schedule.

Language Modelling

Contextual Temperature for Language Modeling

no code implementations25 Dec 2020 Pei-Hsin Wang, Sheng-Iou Hsieh, Shih-Chieh Chang, Yu-Ting Chen, Jia-Yu Pan, Wei Wei, Da-Chang Juan

Temperature scaling has been widely used as an effective approach to control the smoothness of a distribution, which helps the model performance in various tasks.

Language Modelling

Robust Processing-In-Memory Neural Networks via Noise-Aware Normalization

no code implementations7 Jul 2020 Li-Huang Tsai, Shih-Chieh Chang, Yu-Ting Chen, Jia-Yu Pan, Wei Wei, Da-Cheng Juan

In this paper, we propose a noise-agnostic method to achieve robust neural network performance against any noise setting.

Object Detection Semantic Segmentation

Learning with Hierarchical Complement Objective

no code implementations17 Nov 2019 Hao-Yun Chen, Li-Huang Tsai, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Label hierarchies widely exist in many vision-related problems, ranging from explicit label hierarchies existed in image classification to latent label hierarchies existed in semantic segmentation.

General Classification Image Classification +1

Improving Adversarial Robustness via Guided Complement Entropy

2 code implementations ICCV 2019 Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model.

Adversarial Defense Adversarial Robustness

Complement Objective Training

1 code implementation ICLR 2019 Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes.

Natural Language Understanding

Searching Toward Pareto-Optimal Device-Aware Neural Architectures

no code implementations29 Aug 2018 An-Chieh Cheng, Jin-Dong Dong, Chi-Hung Hsu, Shu-Huan Chang, Min Sun, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding.

Image Classification

Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs

no code implementations17 Dec 2017 Yu-Ting Chen, Joseph Wang, Yannan Bai, Gregory Castañón, Venkatesh Saligrama

We present a novel framework for finding complex activities matching user-described queries in cluttered surveillance videos.

Semantic Retrieval

No More Discrimination: Cross City Adaptation of Road Scene Segmenters

9 code implementations ICCV 2017 Yi-Hsin Chen, Wei-Yu Chen, Yu-Ting Chen, Bo-Cheng Tsai, Yu-Chiang Frank Wang, Min Sun

Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases.

Semantic Segmentation

Efficient Training of Very Deep Neural Networks for Supervised Hashing

no code implementations CVPR 2016 Ziming Zhang, Yu-Ting Chen, Venkatesh Saligrama

In this paper, we propose training very deep neural networks (DNNs) for supervised learning of hash codes.

Group Membership Prediction

no code implementations ICCV 2015 Ziming Zhang, Yu-Ting Chen, Venkatesh Saligrama

In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances.

Person Re-Identification

A Novel Visual Word Co-occurrence Model for Person Re-identification

no code implementations24 Oct 2014 Ziming Zhang, Yu-Ting Chen, Venkatesh Saligrama

We first map each pixel of an image to a visual word using a codebook, which is learned in an unsupervised manner.

Person Re-Identification

A Rank-SVM Approach to Anomaly Detection

no code implementations2 May 2014 Jing Qian, Jonathan Root, Venkatesh Saligrama, Yu-Ting Chen

The resulting anomaly detector is shown to be asymptotically optimal and adaptive in that for any false alarm rate alpha, its decision region converges to the alpha-percentile level set of the unknown underlying density.

Anomaly Detection

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