Imbalanced Data Classification Using Auxiliary Classifier Generative Adversarial Networks
نویسندگان
چکیده
منابع مشابه
Automatic Colorization of Grayscale Images Using Generative Adversarial Networks
Automatic colorization of gray scale images poses a unique challenge in Information Retrieval. The goal of this field is to colorize images which have lost some color channels (such as the RGB channels or the AB channels in the LAB color space) while only having the brightness channel available, which is usually the case in a vast array of old photos and portraits. Having the ability to coloriz...
متن کاملTAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions. Former approaches have tried to condition the generative process on the textual data; but allying it to the usage of class information, known to diversify the generated samples and ...
متن کاملData Augmentation in Emotion Classification Using Generative Adversarial Networks
It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an example of imbalanced label distribution, because some classes of emotions like disgusted are relatively rare comparing to other labels like happy or sad. In this paper, we propose a da...
متن کاملIncremental Classifier Learning with Generative Adversarial Networks
In this paper, we address the incremental classifier learning problem, which suffers from catastrophic forgetting. The main reason for catastrophic forgetting is that the past data are not available during learning. Typical approaches keep some exemplars for the past classes and use distillation regularization to retain the classification capability on the past classes and balance the past and ...
متن کاملData Augmentation Generative Adversarial Networks
Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation (Krizhevsky et al., 2012) alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible alternative data. Given there is potential to generate a much broader set of...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Advanced Trends in Computer Science and Engineering
سال: 2020
ISSN: 2278-3091
DOI: 10.30534/ijatcse/2020/26922020