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1. Crowd Counting with Minimal Data Using Generative Adversarial Networks for Multiple Target Regression

   https://doi.org/10.1109/WACV.2018.00131  

   Olmschenk, Greg; Tang, Hao; Zhu, Zhigang (March 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV))

   In this work, we use a generative adversarial network (GAN) to train crowd counting networks using minimal data. We describe how GAN objectives can be modified to allow for the use of unlabeled data to benefit inference training in semi-supervised learning. More generally, we explain how these same methods can be used in more generic multiple regression target semi-supervised learning, with crowd counting being a demonstrative example. Given a convolutional neural network (CNN) with capabilities equivalent to the discriminator in the GAN, we provide experimental results which show that our GAN is able to outperform the CNN even when the CNN has access to significantly more labeled data. This presents the potential of training such networks to high accuracy with little data. Our primary goal is not to outperform the state-of-the-art using an improved method on the entire dataset, but instead we work to show that through semi-supervised learning we can reduce the data required to train an inference network to a given accuracy. To this end, systematic experiments are performed with various numbers of images and cameras to show under which situations the semi-supervised GANs can improve results. 

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2. UNDERSTANDING OVERPARAMETERIZATION IN GENERATIVE ADVERSARIAL NETWORKS

   Balaji, Yogesh; Sajedi, Mohammadmahdi; Kalibhat, Neha Mukund; Ding, Mucong; Stoger, Dominik; Soltanolkotabi, Mahdi; Feizi, Soheil (April 2021, International Conference on Learning Representations)

   null (Ed.)

   A broad class of unsupervised deep learning methods such as Generative Adversarial Networks (GANs) involve training of overparameterized models where the number of parameters of the model exceeds a certain threshold. Indeed, most successful GANs used in practice are trained using overparameterized generator and discriminator networks, both in terms of depth and width. A large body of work in supervised learning have shown the importance of model overparameterization in the convergence of the gradient descent (GD) to globally optimal solutions. In contrast, the unsupervised setting and GANs in particular involve non-convex concave mini-max optimization problems that are often trained using Gradient Descent/Ascent (GDA). The role and benefits of model overparameterization in the convergence of GDA to a global saddle point in non-convex concave problems is far less understood. In this work, we present a comprehensive analysis of the importance of model overparameterization in GANs both theoretically and empirically. We theoretically show that in an overparameterized GAN model with a 1-layer neural network generator and a linear discriminator, GDA converges to a global saddle point of the underlying non-convex concave min-max problem. To the best of our knowledge, this is the first result for global convergence of GDA in such settings. Our theory is based on a more general result that holds for a broader class of nonlinear generators and discriminators that obey certain assumptions (including deeper generators and random feature discriminators). Our theory utilizes and builds upon a novel connection with the convergence analysis of linear timevarying dynamical systems which may have broader implications for understanding the convergence behavior of GDA for non-convex concave problems involving overparameterized models. We also empirically study the role of model overparameterization in GANs using several large-scale experiments on CIFAR-10 and Celeb-A datasets. Our experiments show that overparameterization improves the quality of generated samples across various model architectures and datasets. Remarkably, we observe that overparameterization leads to faster and more stable convergence behavior of GDA across the board. 

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3. Calibrating Uncertainty for Semi-Supervised Crowd Counting

   Li, Chen; Hu, Xiaoling; Abousamra, Shahira; Chen, Chao (October 2023, International Conference on Computer Vision (ICCV))

   Semi-supervised crowd counting is an important yet challenging task. A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set. The key is to use uncertainty to select reliable pseudo-labels. In this paper, we propose a novel method to calibrate model uncertainty for crowd counting. Our method takes a supervised uncertainty estimation strategy to train the model through a surrogate function. This ensures the uncertainty is well controlled throughout the training. We propose a matching-based patch-wise surrogate function to better approximate uncertainty for crowd counting tasks. The proposed method pays a sufficient amount of attention to details, while maintaining a proper granularity. Altogether our method is able to generate reliable uncertainty estimation, high quality pseudolabels, and achieve state-of-the-art performance in semisupervised crowd counting. 

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4. Cross-frequency training with adversarial learning for radar micro-Doppler signature classification (Rising Researcher)

   https://doi.org/10.1117/12.2559155  

   Gurbuz, Sevgi Zubeyde; Rahman, Mohammad M.; Kurtoglu, Emre; Macks, Trevor; Fioranelli, Francesco (May 2020, Proc. SPIE 11408, Radar Sensor Technology XXIV)

   Deep neural networks have become increasingly popular in radar micro-Doppler classification; yet, a key challenge, which has limited potential gains, is the lack of large amounts of measured data that can facilitate the design of deeper networks with greater robustness and performance. Several approaches have been proposed in the literature to address this problem, such as unsupervised pre-training and transfer learning from optical imagery or synthetic RF data. This work investigates an alternative approach to training which involves exploitation of “datasets of opportunity” – micro-Doppler datasets collected using other RF sensors, which may be of a different frequency, bandwidth or waveform - for the purposes of training. Specifically, this work compares in detail the cross-frequency training degradation incurred for several different training approaches and deep neural network (DNN) architectures. Results show a 70% drop in classification accuracy when the RF sensors for pre-training, fine-tuning, and testing are different, and a 15% degradation when only the pre-training data is different, but the fine-tuning and test data are from the same sensor. By using generative adversarial networks (GANs), a large amount of synthetic data is generated for pre-training. Results show that cross-frequency performance degradation is reduced by 50% when kinematically-sifted GAN-synthesized signatures are used in pre-training. 

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5. Physics-Aware Design of Multi-Branch GAN for Human RF Micro-Doppler Signature Synthesis

   https://doi.org/10.1109/RadarConf2147009.2021.9455194  

   Rahman, M. Mahbubur; Gurbuz, Sevgi Z.; Amin, Moeness G. (May 2021, IEEE Radar Conference)

   null (Ed.)

   Generative adversarial networks (GANs) have been recently proposed for the synthesis of RF micro-Doppler signatures to mitigate the problem of low sample support and enable the training of deeper neural networks (DNNs) for improved RF signal classification. However, when applied to human micro-Doppler signatures for gait analysis, GANs suffer from systemic kinematic discrepancies that degrade performance. As a solution to this problem, this paper proposes the design of a physics-aware loss function and multi-branch GAN architecture. Our results show that RF gait signatures synthesized using the proposed approached have greater correlation and similarity to measured RF gait signatures, while also improving the accuracy in classifying five different gaits. 

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