482 citations · 1.4k across the 36 of their papers we have counts for
10 papers · 1 filter
A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition
Govind Narasimman, Kangkang Lu, Arun Raja +4
Despite the vast literature on Human Activity Recognition (HAR) with wearable inertial sensor data, it is perhaps surprising that there are few studies investigating semisupervised…
Empirical Analysis of Overfitting and Mode Drop in GAN Training
Yasin Yazici, Chuan-Sheng Foo, Stefan Winkler +2
We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective. We show that when stochasticity is removed from the training procedur…
Classify and Generate: Using Classification Latent Space Representations for Image Generations
Saisubramaniam Gopalakrishnan, Pranshu Ranjan Singh, Yasin Yazici +3
Utilization of classification latent space information for downstream reconstruction and generation is an intriguing and a relatively unexplored area. In general, discriminative re…
Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions
Yasin Yazıcı, Bruno Lecouat, Chuan-Sheng Foo +4
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities. Each data distribution is modeled with a mixture of $…
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks
Xue Geng, Jie Fu, Bin Zhao +4
This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage.…
Adversarially Learned Anomaly Detection
Houssam Zenati, Manon Romain, Chuan Sheng Foo +2
Anomaly detection is a significant and hence well-studied problem. However, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge…
Manifold regularization with GANs for semi-supervised learning
Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati +1
Generative Adversarial Networks are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization b…
Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati +3
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond. By necess…
Semi-Supervised Learning with GANs: Revisiting Manifold Regularization
Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati +1
GANS are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization by approximating the Laplaci…
Efficient GAN-Based Anomaly Detection
Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat +2
Generative adversarial networks (GANs) are able to model the complex highdimensional distributions of real-world data, which suggests they could be effective for anomaly detection.…