Chuan Sheng Foo

research

∙ 07/14/2023

PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation

Unsupervised domain adaptation (UDA) has witnessed remarkable advancemen...

0 Dapeng Hu, et al. ∙

research

∙ 07/14/2023

Source-Free Domain Adaptation with Temporal Imputation for Time Series Data

Source-free domain adaptation (SFDA) aims to adapt a pretrained model fr...

0 Mohamed Ragab, et al. ∙

research

∙ 04/17/2023

MoDA: Modeling Deformable 3D Objects from Casual Videos

In this paper, we focus on the challenges of modeling deformable 3D obje...

0 Chaoyue Song, et al. ∙

research

∙ 03/24/2023

Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation

Current methods for few-shot segmentation (FSSeg) have mainly focused on...

0 Weide Liu, et al. ∙

research

∙ 01/31/2023

Fourier Sensitivity and Regularization of Computer Vision Models

Recent work has empirically shown that deep neural networks latch on to ...

0 Kiran Krishnamachari, et al. ∙

research

∙ 12/15/2022

Co-Learning with Pre-Trained Networks Improves Source-Free Domain Adaptation

Source-free domain adaptation aims to adapt a source model trained on fu...

0 Wenyu Zhang, et al. ∙

research

∙ 09/29/2022

Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning

Pruning neural networks has become popular in the last decade when it wa...

0 Manas Gupta, et al. ∙

research

∙ 06/16/2022

Domain Generalization via Selective Consistency Regularization for Time Series Classification

Domain generalization methods aim to learn models robust to domain shift...

0 Wenyu Zhang, et al. ∙

research

∙ 05/30/2022

Few-Shot Adaptation of Pre-Trained Networks for Domain Shift

Deep networks are prone to performance degradation when there is a domai...

0 Wenyu Zhang, et al. ∙

research

∙ 05/18/2022

SemiCurv: Semi-Supervised Curvilinear Structure Segmentation

Recent work on curvilinear structure segmentation has mostly focused on ...

19 Xun Xu, et al. ∙

research

∙ 05/06/2022

Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime

Semi-supervised learning (SSL) addresses the lack of labeled data by exp...

0 Xun Xu, et al. ∙

research

∙ 05/02/2022

Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding

Semantic understanding of 3D point cloud relies on learning models with ...

5 Xian Shi, et al. ∙

research

∙ 03/15/2022

ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data

Unsupervised domain adaptation methods aim to generalize well on unlabel...

0 Mohamed Ragab, et al. ∙

research

∙ 12/11/2021

On Automatic Data Augmentation for 3D Point Cloud Classification

Data augmentation is an important technique to reduce overfitting and im...

0 Wanyue Zhang, et al. ∙

research

∙ 11/09/2021

On Representation Knowledge Distillation for Graph Neural Networks

Knowledge distillation is a promising learning paradigm for boosting the...

0 Chaitanya K. Joshi, et al. ∙

research

∙ 09/23/2021

An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series

Several techniques for multivariate time series anomaly detection have b...

0 Astha Garg, et al. ∙

research

∙ 08/10/2021

Semi-supervised classification of radiology images with NoTeacher: A Teacher that is not Mean

Deep learning models achieve strong performance for radiology image clas...

0 Balagopal Unnikrishnan, et al. ∙

research

∙ 08/04/2021

Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds

Recently deep learning has achieved significant progress on point cloud ...

0 Fayao Liu, et al. ∙

research

∙ 01/18/2021

Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach

Semantic segmentation of 3D point clouds relies on training deep models ...

15 Xian Shi, et al. ∙

research

∙ 01/13/2021

A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition

Despite the vast literature on Human Activity Recognition (HAR) with wea...

0 Govind Narasimman, et al. ∙

research

∙ 06/25/2020

Empirical Analysis of Overfitting and Mode Drop in GAN Training

We examine two key questions in GAN training, namely overfitting and mod...

0 Yasin Yazıcı, et al. ∙

research

∙ 04/16/2020

Classification Representations Can be Reused for Downstream Generations

Contrary to the convention of using supervision for class-conditioned ge...

1 Saisubramaniam Gopalakrishnan, et al. ∙

research

∙ 02/13/2020

Scalable and Practical Natural Gradient for Large-Scale Deep Learning

Large-scale distributed training of deep neural networks results in mode...

0 Kazuki Osawa, et al. ∙

research

∙ 12/22/2019

Learning to Impute: A General Framework for Semi-supervised Learning

Recent semi-supervised learning methods have shown to achieve comparable...

9 Wei-Hong Li, et al. ∙

research

∙ 02/09/2019

Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions

We propose a GAN design which models multiple distributions effectively ...

0 Yasin Yazıcı, et al. ∙

research

∙ 12/19/2018

Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images

Supervised deep learning algorithms have enabled significant performance...

14 Bruno Lecouat, et al. ∙

research

∙ 12/06/2018

Adversarially Learned Anomaly Detection

Anomaly detection is a significant and hence well-studied problem. Howev...

26 Houssam Zenati, et al. ∙

research

∙ 11/29/2018

TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks

Embedded deep learning platforms have witnessed two simultaneous improve...

0 Lile Cai, et al. ∙

research

∙ 11/15/2018

Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials

We introduce the use of Crystal Graph Convolutional Neural Networks (CGC...

0 Leo Laugier, et al. ∙

research

∙ 11/12/2018

Holistic Multi-modal Memory Network for Movie Question Answering

Answering questions according to multi-modal context is a challenging pr...

12 Anran Wang, et al. ∙

research

∙ 07/11/2018

Manifold regularization with GANs for semi-supervised learning

Generative Adversarial Networks are powerful generative models that are ...

0 Bruno Lecouat, et al. ∙

research

∙ 07/07/2018

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile

Owing to their connection with generative adversarial networks (GANs), s...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 07/07/2018

Mirror descent in saddle-point problems: Going the extra (gradient) mile

Owing to their connection with generative adversarial networks (GANs), s...

0 Panayotis Mertikopoulos, et al. ∙

research

∙ 06/12/2018

The Unusual Effectiveness of Averaging in GAN Training

We show empirically that the optimal strategy of parameter averaging in ...

0 Yasin Yazıcı, et al. ∙

research

∙ 05/23/2018

Semi-Supervised Learning with GANs: Revisiting Manifold Regularization

GANS are powerful generative models that are able to model the manifold ...

0 Bruno Lecouat, et al. ∙

research

∙ 02/17/2018

Efficient GAN-Based Anomaly Detection

Generative adversarial networks (GANs) are able to model the complex hig...

0 Houssam Zenati, et al. ∙

Chuan Sheng Foo

Featured Co-authors