scholarly journals Symmetrical Synthesis for Deep Metric Learning

2020 ◽  
Vol 34 (07) ◽  
pp. 10853-10860
Author(s):  
Geonmo Gu ◽  
Byungsoo Ko
Keyword(s):  
Metric Learning ◽  
Generative Adversarial Networks ◽  
Learning Loss ◽  
Adversarial Networks ◽  
Data Points ◽  
Novel Method ◽  
Deep Metric Learning ◽  
Similarity Information ◽  
Negative Pair ◽  
Original Feature

Deep metric learning aims to learn embeddings that contain semantic similarity information among data points. To learn better embeddings, methods to generate synthetic hard samples have been proposed. Existing methods of synthetic hard sample generation are adopting autoencoders or generative adversarial networks, but this leads to more hyper-parameters, harder optimization, and slower training speed. In this paper, we address these problems by proposing a novel method of synthetic hard sample generation called symmetrical synthesis. Given two original feature points from the same class, the proposed method firstly generates synthetic points with each other as an axis of symmetry. Secondly, it performs hard negative pair mining within the original and synthetic points to select a more informative negative pair for computing the metric learning loss. Our proposed method is hyper-parameter free and plug-and-play for existing metric learning losses without network modification. We demonstrate the superiority of our proposed method over existing methods for a variety of loss functions on clustering and image retrieval tasks.

scholarly journals Dynamics of Fourier Modes in Torus Generative Adversarial Networks

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 325
Author(s):  
Ángel González-Prieto ◽  
Alberto Mozo ◽  
Edgar Talavera ◽  
Sandra Gómez-Canaval
Keyword(s):  
Fourier Series ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Training Process ◽  
Small Perturbations ◽  
Adversarial Networks ◽  
Novel Method ◽  
Truncated Fourier Series ◽  
Real Flow ◽  
Machine Learning Models

Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution. Despite their success, the training process of a GAN is highly unstable, and typically, it is necessary to implement several accessory heuristics to the networks to reach acceptable convergence of the model. In this paper, we introduce a novel method to analyze the convergence and stability in the training of generative adversarial networks. For this purpose, we propose to decompose the objective function of the adversary min–max game defining a periodic GAN into its Fourier series. By studying the dynamics of the truncated Fourier series for the continuous alternating gradient descend algorithm, we are able to approximate the real flow and to identify the main features of the convergence of GAN. This approach is confirmed empirically by studying the training flow in a 2-parametric GAN, aiming to generate an unknown exponential distribution. As a by-product, we show that convergent orbits in GANs are small perturbations of periodic orbits so the Nash equillibria are spiral attractors. This theoretically justifies the slow and unstable training observed in GANs.

scholarly journals Modelling and forecasting art movements with CGANs

2020 ◽  
Vol 7 (4) ◽  
pp. 191569
Author(s):  
Edoardo Lisi ◽  
Mohammad Malekzadeh ◽  
Hamed Haddadi ◽  
F. Din-Houn Lau ◽  
Seth Flaxman
Keyword(s):  
Generative Adversarial Networks ◽  
Vector Autoregressive ◽  
Adversarial Networks ◽  
Latent Distribution ◽  
Small Set ◽  
One Step ◽  
Temporal Aspect ◽  
Novel Method ◽  
The Mean ◽  
Modelling And Forecasting

Conditional generative adversarial networks (CGANs) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The latent information often comes in the form of a discrete label from a small set. We propose a novel method for training CGANs which allows us to condition on a sequence of continuous latent distributions f (1) , …, f ( K ) . This training allows CGANs to generate samples from a sequence of distributions. We apply our method to paintings from a sequence of artistic movements, where each movement is considered to be its own distribution. Exploiting the temporal aspect of the data, a vector autoregressive (VAR) model is fitted to the means of the latent distributions that we learn, and used for one-step-ahead forecasting, to predict the latent distribution of a future art movement f ( K +1) . Realizations from this distribution can be used by the CGAN to generate ‘future’ paintings. In experiments, this novel methodology generates accurate predictions of the evolution of art. The training set consists of a large dataset of past paintings. While there is no agreement on exactly what current art period we find ourselves in, we test on plausible candidate sets of present art, and show that the mean distance to our predictions is small.

scholarly journals Deep Metric Learning by Online Soft Mining and Class-Aware Attention

2019 ◽  
Vol 33 ◽  
pp. 5361-5368 ◽  
Author(s):  
Xinshao Wang ◽  
Yang Hua ◽  
Elyor Kodirov ◽  
Guosheng Hu ◽  
Neil M. Robertson
Keyword(s):  
Large Fraction ◽  
Metric Learning ◽  
Improve Performance ◽  
Fine Grained ◽  
Training Samples ◽  
Deep Embedding ◽  
Data Points ◽  
Accelerate Convergence ◽  
Deep Metric Learning ◽  
Mining Methods

Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learning is known to suffer from slow convergence due to a large fraction of trivial samples. Therefore, most existing methods generally resort to sample mining strategies for selecting nontrivial samples to accelerate convergence and improve performance. In this work, we identify two critical limitations of the sample mining methods, and provide solutions for both of them. First, previous mining methods assign one binary score to each sample, i.e., dropping or keeping it, so they only selects a subset of relevant samples in a mini-batch. Therefore, we propose a novel sample mining method, called Online Soft Mining (OSM), which assigns one continuous score to each sample to make use of all samples in the mini-batch. OSM learns extended manifolds that preserve useful intraclass variances by focusing on more similar positives. Second, the existing methods are easily influenced by outliers as they are generally included in the mined subset. To address this, we introduce Class-Aware Attention (CAA) that assigns little attention to abnormal data samples. Furthermore, by combining OSM and CAA, we propose a novel weighted contrastive loss to learn discriminative embeddings. Extensive experiments on two fine-grained visual categorisation datasets and two video-based person re-identification benchmarks show that our method significantly outperforms the state-of-the-art.

scholarly journals Evolving optimum camouflage with Generative Adversarial Networks

2018 ◽  
Author(s):  
Laszlo Talas ◽  
John G. Fennell ◽  
Karin Kjernsmo ◽  
Innes C. Cuthill ◽  
Nicholas E. Scott-Samuel ◽  
...  
Keyword(s):  
Arms Race ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Novel Method ◽  
Multiple Environments ◽  
Evolutionary Arms Race

AbstractWe describe a novel method to exploit Generative Adversarial Networks to simulate an evolutionary arms race between the camouflage of a synthetic prey and its predator. Patterns evolved using our methods are shown to provide progressively more effective concealment and outperform two recognised camouflage techniques. The method will be invaluable, particularly for biologists, for rapidly developing and testing optimal camouflage or signalling patterns in multiple environments.

Improving Cloud-based ECG Monitoring, Detection and Classification using GAN

2020 ◽  
pp. 42-49
Author(s):  
admin admin ◽  
◽  
◽  
Monika Gupta
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Learning Models ◽  
Healthcare Applications ◽  
Ecg Signals ◽  
Cardiac Abnormalities ◽  
Adversarial Networks ◽  
Data Points

Internet of Things (IoT) based healthcare applications have grown exponentially over the past decade. With the increasing number of fatalities due to cardiovascular diseases (CVD), it is the need of the hour to detect any signs of cardiac abnormalities as early as possible. This calls for automation on the detection and classification of said cardiac abnormalities by physicians. The problem here is that, there is not enough data to train Deep Learning models to classify ECG signals accurately because of sensitive nature of data and the rarity of certain cases involved in CVDs. In this paper, we propose a framework which involves Generative Adversarial Networks (GAN) to create synthetic training data for the classes with less data points to improve the performance of Deep Learning models trained with the dataset. With data being input from sensors via cloud and this model to classify the ECG signals, we expect the framework to be functional, accurate and efficient.

scholarly journals FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 807 ◽  
Author(s):  
Weiwei Zhuang ◽  
Liang Chen ◽  
Chaoqun Hong ◽  
Yuxin Liang ◽  
Keshou Wu
Keyword(s):  
Face Recognition ◽  
Generative Adversarial Networks ◽  
Facial Features ◽  
Adversarial Networks ◽  
Key Points ◽  
In The Wild ◽  
Novel Method ◽  
Face Synthesis ◽  
Unconstrained Face

Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. It is based on face transformation with key points alignment based on generative adversarial networks (FT-GAN). In this method, we introduce CycleGAN for pixel transformation to achieve coarse face transformation results, and these results are refined by key point alignment. In this way, frontal face synthesis is modeled as a two-task process. The results of comprehensive experiments show the effectiveness of FT-GAN.

scholarly journals GENERATIVE ADVERSARIAL NETWORKS AS A NOVEL APPROACH FOR TECTONIC FAULT AND FRACTURE EXTRACTION IN HIGH-RESOLUTION SATELLITE AND AIRBORNE OPTICAL IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1219-1227
Author(s):  
B. Jafrasteh ◽  
I. Manighetti ◽  
J. Zerubia
Keyword(s):  
Ground Truth ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Convolutional Networks ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Optical Images ◽  
Novel Approach ◽  
Ground Truth Image ◽  
Novel Method

Abstract. We develop a novel method based on Deep Convolutional Networks (DCN) to automate the identification and mapping of fracture and fault traces in optical images. The method employs two DCNs in a two players game: a first network, called Generator, learns to segment images to make them resembling the ground truth; a second network, called Discriminator, measures the differences between the ground truth image and each segmented image and sends its score feedback to the Generator; based on these scores, the Generator improves its segmentation progressively. As we condition both networks to the ground truth images, the method is called Conditional Generative Adversarial Network (CGAN). We propose a new loss function for both the Generator and the Discriminator networks, to improve their accuracy. Using two criteria and a manually annotated optical image, we compare the generalization performance of the proposed method to that of a classical DCN architecture, U-net. The comparison demonstrates the suitability of the proposed CGAN architecture. Further work is however needed to improve its efficiency.

scholarly journals Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks

2018 ◽  
Author(s):  
Feiwu Yu ◽  
Xinxiao Wu ◽  
Yuchao Sun ◽  
Lixin Duan
Keyword(s):  
Domain Adaptation ◽  
Feature Representation ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Feature Representations ◽  
Adversarial Networks ◽  
Video Recognition ◽  
Novel Method ◽  
High Level ◽  
Heterogeneous Feature

Existing deep learning methods of video recognition usually require a large number of labeled videos for training. But for a new task, videos are often unlabeled and it is also time-consuming and labor-intensive to annotate them. Instead of human annotation, we try to make use of existing fully labeled images to help recognize those videos. However, due to the problem of domain shifts and heterogeneous feature representations, the performance of classifiers trained on images may be dramatically degraded for video recognition tasks. In this paper, we propose a novel method, called Hierarchical Generative Adversarial Networks (HiGAN), to enhance recognition in videos (i.e., target domain) by transferring knowledge from images (i.e., source domain). The HiGAN model consists of a \emph{low-level} conditional GAN and a \emph{high-level} conditional GAN. By taking advantage of these two-level adversarial learning, our method is capable of learning a domain-invariant feature representation of source images and target videos. Comprehensive experiments on two challenging video recognition datasets (i.e. UCF101 and HMDB51) demonstrate the effectiveness of the proposed method when compared with the existing state-of-the-art domain adaptation methods.

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