scholarly journals A New Strategy for One-Example Person re-ID: Exploit the Unlabeled Data Gradually Base on Style-Transferred Images

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 689
Author(s):  
Qiang Li ◽  
Ling Gao
Keyword(s):  
Computer Vision ◽  
State Of The Art ◽  
Recognition Rate ◽  
Research Field ◽  
Generative Adversarial Networks ◽  
Learning Framework ◽  
Adversarial Networks ◽  
New Strategy ◽  
Labeling Scheme ◽  
Self Learning

As a research field of symmetry journals, computer vision has received more and more attention. Person re-identification (re-ID) has become a research hotspot in computer vision. We focus on one-example person re-ID, where each person only has one labeled image in the dataset, and other images are unlabeled. There are two main challenges of the task, the insufficient labeled data, and the lack of labeled images cross-cameras. In dealing with the above issue, we propose a new one-example labeling scheme, which generates style-transferred images by CycleGAN (Cycle Generative Adversarial Networks) to ensure that for each person, there is one labeled image under each camera style. Then a self-learning framework is adopted, which iteratively train a CNN (Convolutional Neural Networks) model with labeled images and labeled style-transferred images, and mine the reliable images to assign a pseudo label. The experimental results prove that by integrating the camera style transferred images, we effectively expand the dataset, and the problem of low recognition rate caused by the lack of labeled pedestrian pictures across cameras is effectively solved. Notably, the rank-1 accuracy of our method outperforms the state-of-the-art method by 8.7 points on the Market-1501 dataset, and 6.3 points on the DukeMTMC-ReID dataset.

scholarly journals Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software

2020 ◽  
Vol 34 (07) ◽  
pp. 11296-11303 ◽  
Author(s):  
Satoshi Kosugi ◽  
Toshihiko Yamasaki
Keyword(s):  
Reinforcement Learning ◽  
Image Enhancement ◽  
High Performance ◽  
State Of The Art ◽  
Mapping Function ◽  
Generative Adversarial Networks ◽  
Image Editing ◽  
Learning Framework ◽  
Adversarial Networks ◽  
Image Pairs

This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs. Our method is based on generative adversarial networks (GANs), but instead of simply generating images with a neural network, we enhance images utilizing image editing software such as Adobe® Photoshop® for the following three benefits: enhanced images have no artifacts, the same enhancement can be applied to larger images, and the enhancement is interpretable. To incorporate image editing software into a GAN, we propose a reinforcement learning framework where the generator works as the agent that selects the software's parameters and is rewarded when it fools the discriminator. Our framework can use high-quality non-differentiable filters present in image editing software, which enables image enhancement with high performance. We apply the proposed method to two unpaired image enhancement tasks: photo enhancement and face beautification. Our experimental results demonstrate that the proposed method achieves better performance, compared to the performances of the state-of-the-art methods based on unpaired learning.

scholarly journals Improving Skin Lesion Analysis with Generative Adversarial Networks

2020 ◽  
Author(s):  
Alceu Bissoto ◽  
Sandra Avila
Keyword(s):  
Skin Lesion ◽  
State Of The Art ◽  
Synthetic Data ◽  
Clinical Information ◽  
Analysis Data ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Classification Models ◽  
Adversarial Networks ◽  
Lesion Analysis

Melanoma is the most lethal type of skin cancer. Early diagnosis is crucial to increase the survival rate of those patients due to the possibility of metastasis. Automated skin lesion analysis can play an essential role by reaching people that do not have access to a specialist. However, since deep learning became the state-of-the-art for skin lesion analysis, data became a decisive factor in pushing the solutions further. The core objective of this M.Sc. dissertation is to tackle the problems that arise by having limited datasets. In the first part, we use generative adversarial networks to generate synthetic data to augment our classification model’s training datasets to boost performance. Our method generates high-resolution clinically-meaningful skin lesion images, that when compound our classification model’s training dataset, consistently improved the performance in different scenarios, for distinct datasets. We also investigate how our classification models perceived the synthetic samples and how they can aid the model’s generalization. Finally, we investigate a problem that usually arises by having few, relatively small datasets that are thoroughly re-used in the literature: bias. For this, we designed experiments to study how our models’ use data, verifying how it exploits correct (based on medical algorithms), and spurious (based on artifacts introduced during image acquisition) correlations. Disturbingly, even in the absence of any clinical information regarding the lesion being diagnosed, our classification models presented much better performance than chance (even competing with specialists benchmarks), highly suggesting inflated performances.

scholarly journals Multi-Turn Chatbot Based on Query-Context Attentions and Dual Wasserstein Generative Adversarial Networks

2019 ◽  
Vol 9 (18) ◽  
pp. 3908 ◽  
Author(s):  
Jintae Kim ◽  
Shinhyeok Oh ◽  
Oh-Woog Kwon ◽  
Harksoo Kim
Keyword(s):  
Performance Measures ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Training Method ◽  
Adversarial Networks ◽  
Proposed Model ◽  
Previous State ◽  
Vector Representations

To generate proper responses to user queries, multi-turn chatbot models should selectively consider dialogue histories. However, previous chatbot models have simply concatenated or averaged vector representations of all previous utterances without considering contextual importance. To mitigate this problem, we propose a multi-turn chatbot model in which previous utterances participate in response generation using different weights. The proposed model calculates the contextual importance of previous utterances by using an attention mechanism. In addition, we propose a training method that uses two types of Wasserstein generative adversarial networks to improve the quality of responses. In experiments with the DailyDialog dataset, the proposed model outperformed the previous state-of-the-art models based on various performance measures.

scholarly journals Recent Advances of Generative Adversarial Networks in Computer Vision

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 14985-15006 ◽  
Author(s):  
Yang-Jie Cao ◽  
Li-Li Jia ◽  
Yong-Xia Chen ◽  
Nan Lin ◽  
Cong Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Recent Advances

scholarly journals Guest Editorial: Generative Adversarial Networks for Computer Vision

2020 ◽  
Vol 128 (10-11) ◽  
pp. 2363-2365
Author(s):  
Jun-Yan Zhu ◽  
Hongsheng Li ◽  
Eli Shechtman ◽  
Ming-Yu Liu ◽  
Jan Kautz ◽  
...  
Keyword(s):  
Computer Vision ◽  
Guest Editorial ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Generating Adversarial Examples with Adversarial Networks

2018 ◽  
Author(s):  
Chaowei Xiao ◽  
Bo Li ◽  
Jun-yan Zhu ◽  
Warren He ◽  
Mingyan Liu ◽  
...  
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Black Box ◽  
Generative Adversarial Networks ◽  
Perceptual Quality ◽  
Small Magnitude ◽  
Adversarial Networks ◽  
Original Target ◽  
Adversarial Examples ◽  
Adversarial Training

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial exam- ples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply Adv- GAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.

scholarly journals CAGAN: Consistent Adversarial Training Enhanced GANs

2018 ◽  
Author(s):  
Yao Ni ◽  
Dandan Song ◽  
Xi Zhang ◽  
Hao Wu ◽  
Lejian Liao
Keyword(s):  
Neural Network ◽  
Parameter Space ◽  
Supervised Classification ◽  
State Of The Art ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Real Samples ◽  
Adversarial Networks ◽  
Novel Approach ◽  
Adversarial Training

Generative adversarial networks (GANs) have shown impressive results, however, the generator and the discriminator are optimized in finite parameter space which means their performance still need to be improved. In this paper, we propose a novel approach of adversarial training between one generator and an exponential number of critics which are sampled from the original discriminative neural network via dropout. As discrepancy between outputs of different sub-networks of a same sample can measure the consistency of these critics, we encourage the critics to be consistent to real samples and inconsistent to generated samples during training, while the generator is trained to generate consistent samples for different critics. Experimental results demonstrate that our method can obtain state-of-the-art Inception scores of 9.17 and 10.02 on supervised CIFAR-10 and unsupervised STL-10 image generation tasks, respectively, as well as achieve competitive semi-supervised classification results on several benchmarks. Importantly, we demonstrate that our method can maintain stability in training and alleviate mode collapse.

scholarly journals Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification

2018 ◽  
Author(s):  
Zhong Qian ◽  
Peifeng Li ◽  
Yue Zhang ◽  
Guodong Zhou ◽  
Qiaoming Zhu
Keyword(s):  
State Of The Art ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
Semantic Task ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Traditional Research ◽  
Syntactic Information ◽  
Event Factuality

Event factuality identification is an important semantic task in NLP. Traditional research heavily relies on annotated texts. This paper proposes a two-step framework, first extracting essential factors related with event factuality from raw texts as the input, and then identifying the factuality of events via a Generative Adversarial Network with Auxiliary Classification (AC-GAN). The use of AC-GAN allows the model to learn more syntactic information and address the imbalance among factuality values. Experimental results on FactBank show that our method significantly outperforms several state-of-the-art baselines, particularly on events with embedded sources, speculative and negative factuality values.

scholarly journals Successive Image Generation from a Single Sentence

2021 ◽  
Vol 40 ◽  
pp. 03017
Author(s):  
Amogh Parab ◽  
Ananya Malik ◽  
Arish Damania ◽  
Arnav Parekhji ◽  
Pranit Bari
Keyword(s):  
State Of The Art ◽  
Image Synthesis ◽  
Image Sequence ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Single Sentence ◽  
Adversarial Networks ◽  
Successive Image ◽  
Diagrammatic Representations ◽  
Transfer Of Information

Through various examples in history such as the early man’s carving on caves, dependence on diagrammatic representations, the immense popularity of comic books we have seen that vision has a higher reach in communication than written words. In this paper, we analyse and propose a new task of transfer of information from text to image synthesis. Through this paper we aim to generate a story from a single sentence and convert our generated story into a sequence of images. We plan to use state of the art technology to implement this task. With the advent of Generative Adversarial Networks text to image synthesis have found a new awakening. We plan to take this task a step further, in order to automate the entire process. Our system generates a multi-lined story given a single sentence using a deep neural network. This story is then fed into our networks of multiple stage GANs inorder to produce a photorealistic image sequence.

scholarly journals Content Fidelity of Deep Learning Methods for Clipping and Over-exposure Correction

2021 ◽  
Vol 2021 (1) ◽  
pp. 43-48
Author(s):  
Mekides Assefa Abebe
Keyword(s):  
Computer Vision ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Tone Mapping ◽  
Objective Functions ◽  
Related Research ◽  
Adversarial Networks ◽  
Computer Vision Applications ◽  
Research Domains

Exposure problems, due to standard camera sensor limitations, often lead to image quality degradations such as loss of details and change in color appearance. The quality degradations further hiders the performances of imaging and computer vision applications. Therefore, the reconstruction and enhancement of uderand over-exposed images is essential for various applications. Accordingly, an increasing number of conventional and deep learning reconstruction approaches have been introduced in recent years. Most conventional methods follow color imaging pipeline, which strongly emphasize on the reconstructed color and content accuracy. The deep learning (DL) approaches have conversely shown stronger capability on recovering lost details. However, the design of most DL architectures and objective functions don’t take color fidelity into consideration and, hence, the analysis of existing DL methods with respect to color and content fidelity will be pertinent. Accordingly, this work presents performance evaluation and results of recent DL based overexposure reconstruction solutions. For the evaluation, various datasets from related research domains were merged and two generative adversarial networks (GAN) based models were additionally adopted for tone mapping application scenario. Overall results show various limitations, mainly for severely over-exposed contents, and a promising potential for DL approaches, GAN, to reconstruct details and appearance.

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