【论文推荐】最新七篇图像分类相关论文—条件标签空间、生成对抗胶囊网络、深度预测编码网络、生成对抗网络、数字病理图像、在线表示学习

2018 年 3 月 3 日 专知专知内容组

【导读】专知内容组整理了最近七篇图像分类（Image Classification）相关文章，为大家进行介绍，欢迎查看!

1. Learning Image Conditioned Label Space for Multilabel Classification（学习图像条件标签空间的多标签分类）

作者：Yi-Nan Li,Mei-Chen Yeh

摘要：This work addresses the task of multilabel image classification. Inspired by the great success from deep convolutional neural networks (CNNs) for single-label visual-semantic embedding, we exploit extending these models for multilabel images. Specifically, we propose an image-dependent ranking model, which returns a ranked list of labels according to its relevance to the input image. In contrast to conventional CNN models that learn an image representation (i.e. the image embedding vector), the developed model learns a mapping (i.e. a transformation matrix) from an image in an attempt to differentiate between its relevant and irrelevant labels. Despite the conceptual simplicity of our approach, experimental results on a public benchmark dataset demonstrate that the proposed model achieves state-of-the-art performance while using fewer training images than other multilabel classification methods.

期刊：arXiv, 2018年2月21日

网址：

http://www.zhuanzhi.ai/document/4ec9a46b363cf204902fe1f618cc235c

2. CapsuleGAN: Generative Adversarial Capsule Network（CapsuleGAN:生成对抗胶囊网络）

作者：Ayush Jaiswal,Wael AbdAlmageed,Premkumar Natarajan

摘要：We present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adversarial network (GAN) setting, while modeling image data. We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models. We show that CapsuleGAN outperforms convolutional-GAN at modeling image data distribution on the MNIST dataset of handwritten digits, evaluated on the generative adversarial metric and at semi-supervised image classification.

期刊：arXiv, 2018年2月17日

网址：

http://www.zhuanzhi.ai/document/8fcad4a74a3e0ea8d982f8e4d9016d42

3. Deep Predictive Coding Network for Object Recognition（基于深度预测编码网络的目标识别）

作者：Haiguang Wen,Kuan Han,Junxing Shi,Yizhen Zhang,Eugenio Culurciello,Zhongming Liu

摘要：Inspired by predictive coding in neuroscience, we designed a bi-directional and recurrent neural net, namely deep predictive coding networks (PCN). It uses convolutional layers in both feedforward and feedback networks, and recurrent connections within each layer. Feedback connections from a higher layer carry the prediction of its lower-layer representation; feedforward connections carry the prediction errors to its higher-layer. Given image input, PCN runs recursive cycles of bottom-up and top-down computation to update its internal representations to reduce the difference between bottom-up input and top-down prediction at every layer. After multiple cycles of recursive updating, the representation is used for image classification. In training, the classification error backpropagates across layers and in time. With benchmark data (CIFAR-10/100, SVHN, and MNIST), PCN was found to always outperform its feedforward-only counterpart: a model without any mechanism for recurrent dynamics, and its performance tended to improve given more cycles of computation over time. In short, PCN reuses a single architecture to recursively run bottom-up and top-down process, enabling an increasingly longer cascade of non-linear transformation. For image classification, PCN refines its representation over time towards more accurate and definitive recognition.

期刊：arXiv, 2018年2月14日

网址：

http://www.zhuanzhi.ai/document/1d67648580510cd1e3fb3ccf56129559

4. Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification（基于生成对抗网络和概率图模型的高光谱图像分类）

作者：Zilong Zhong,Jonathan Li

摘要：High spectral dimensionality and the shortage of annotations make hyperspectral image (HSI) classification a challenging problem. Recent studies suggest that convolutional neural networks can learn discriminative spatial features, which play a paramount role in HSI interpretation. However, most of these methods ignore the distinctive spectral-spatial characteristic of hyperspectral data. In addition, a large amount of unlabeled data remains an unexploited gold mine for efficient data use. Therefore, we proposed an integration of generative adversarial networks (GANs) and probabilistic graphical models for HSI classification. Specifically, we used a spectral-spatial generator and a discriminator to identify land cover categories of hyperspectral cubes. Moreover, to take advantage of a large amount of unlabeled data, we adopted a conditional random field to refine the preliminary classification results generated by GANs. Experimental results obtained using two commonly studied datasets demonstrate that the proposed framework achieved encouraging classification accuracy using a small number of data for training.

期刊：arXiv, 2018年2月10日

网址：

http://www.zhuanzhi.ai/document/ab84d984e0296fe26cb8891a1bf33760

5. Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation（融合神经网络的数字病理图像分类和分割）

作者：Gleb Makarchuk,Vladimir Kondratenko,Maxim Pisov,Artem Pimkin,Egor Krivov,Mikhail Belyaev

摘要：In the last years, neural networks have proven to be a powerful framework for various image analysis problems. However, some application domains have specific limitations. Notably, digital pathology is an example of such fields due to tremendous image sizes and quite limited number of training examples available. In this paper, we adopt state-of-the-art convolutional neural networks (CNN) architectures for digital pathology images analysis. We propose to classify image patches to increase effective sample size and then to apply an ensembling technique to build prediction for the original images. To validate the developed approaches, we conducted experiments with \textit{Breast Cancer Histology Challenge} dataset and obtained 90\% accuracy for the 4-class tissue classification task.

期刊：arXiv, 2018年2月3日

网址：

http://www.zhuanzhi.ai/document/9194c903041adf69d4b0dcd01a3ae8f3

6. Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification（基于深度学习框架的多类乳腺癌组织学图像分类）

作者：Yeeleng S. Vang,Zhen Chen,Xiaohui Xie

摘要：In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology images (BACH). As these histology images are too large to fit into GPU memory, we first propose using Inception V3 to perform patch level classification. The patch level predictions are then passed through an ensemble fusion framework involving majority voting, gradient boosting machine (GBM), and logistic regression to obtain the image level prediction. We improve the sensitivity of the Normal and Benign predicted classes by designing a Dual Path Network (DPN) to be used as a feature extractor where these extracted features are further sent to a second layer of ensemble prediction fusion using GBM, logistic regression, and support vector machine (SVM) to refine predictions. Experimental results demonstrate our framework shows a 12.5$\%$ improvement over the state-of-the-art model.

期刊：arXiv, 2018年2月3日

网址：

http://www.zhuanzhi.ai/document/b2b30e84baa2d1d6d15db3602398286c

7. Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification（采用单层和多层Hebbian网络的在线表示学习方法的图像分类）

作者：Yanis Bahroun,Andrea Soltoggio

摘要：Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different data sets using the same underlying rules thanks to the autonomous discovery of discriminating features during training. Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching cost-function. These have been shown to perform sparse representation learning. This study tests the effectiveness of one such learning rule for learning features from images. The rule implemented is derived from a nonnegative classical multidimensional scaling cost-function, and is applied to both single and multi-layer architectures. The features learned by the algorithm are then used as input to an SVM to test their effectiveness in classification on the established CIFAR-10 image dataset. The algorithm performs well in comparison to other unsupervised learning algorithms and multi-layer networks, thus suggesting its validity in the design of a new class of compact, online learning networks.