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07/14/2023
PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation
Unsupervised domain adaptation (UDA) has witnessed remarkable advancemen...
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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...
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04/17/2023
MoDA: Modeling Deformable 3D Objects from Casual Videos
In this paper, we focus on the challenges of modeling deformable 3D obje...
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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...
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01/31/2023
Fourier Sensitivity and Regularization of Computer Vision Models
Recent work has empirically shown that deep neural networks latch on to ...
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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...
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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...
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06/16/2022
Domain Generalization via Selective Consistency Regularization for Time Series Classification
Domain generalization methods aim to learn models robust to domain shift...
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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...
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05/18/2022
SemiCurv: Semi-Supervised Curvilinear Structure Segmentation
Recent work on curvilinear structure segmentation has mostly focused on ...
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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...
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05/02/2022
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding
Semantic understanding of 3D point cloud relies on learning models with ...
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03/15/2022
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
Unsupervised domain adaptation methods aim to generalize well on unlabel...
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12/11/2021
On Automatic Data Augmentation for 3D Point Cloud Classification
Data augmentation is an important technique to reduce overfitting and im...
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11/09/2021
On Representation Knowledge Distillation for Graph Neural Networks
Knowledge distillation is a promising learning paradigm for boosting the...
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09/23/2021
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series
Several techniques for multivariate time series anomaly detection have b...
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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...
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08/04/2021
Point Discriminative Learning for Unsupervised Representation Learning on 3D Point Clouds
Recently deep learning has achieved significant progress on point cloud ...
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01/18/2021
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach
Semantic segmentation of 3D point clouds relies on training deep models ...
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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...
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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...
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04/16/2020
Classification Representations Can be Reused for Downstream Generations
Contrary to the convention of using supervision for class-conditioned ge...
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02/13/2020
Scalable and Practical Natural Gradient for Large-Scale Deep Learning
Large-scale distributed training of deep neural networks results in mode...
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12/22/2019
Learning to Impute: A General Framework for Semi-supervised Learning
Recent semi-supervised learning methods have shown to achieve comparable...
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02/09/2019
Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions
We propose a GAN design which models multiple distributions effectively ...
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12/19/2018
Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
Supervised deep learning algorithms have enabled significant performance...
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12/06/2018
Adversarially Learned Anomaly Detection
Anomaly detection is a significant and hence well-studied problem. Howev...
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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...
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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...
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11/12/2018
Holistic Multi-modal Memory Network for Movie Question Answering
Answering questions according to multi-modal context is a challenging pr...
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07/11/2018
Manifold regularization with GANs for semi-supervised learning
Generative Adversarial Networks are powerful generative models that are ...
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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...
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07/07/2018
Mirror descent in saddle-point problems: Going the extra (gradient) mile
Owing to their connection with generative adversarial networks (GANs), s...
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06/12/2018
The Unusual Effectiveness of Averaging in GAN Training
We show empirically that the optimal strategy of parameter averaging in ...
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05/23/2018
Semi-Supervised Learning with GANs: Revisiting Manifold Regularization
GANS are powerful generative models that are able to model the manifold ...
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02/17/2018