## 1 Introduction  
Cervical cancer continues to be one of the prevalent cancers affecting women worldwide [1]. The disease is the most common cancer among women in 39 countries, and is the leading cause of cancer dearth in women in 45 countries [2]. The disease affects predominantly women in lower-resource countries, almost 70% of the global burden occurs in areas with low or medium levels of human development [2]. Manually screening abnormal cells from a pap-smear image has been a widely accepted method for prevention management as well as early detection of cervical cancer, especially in the developing countries [3]. However, manual assessment has many drawbacks in terms of being labor intensive, tedious, and time-consuming [3]. More importantly, the complex nature of cervical cell images presents significant challenges for manual screen analysis, the diagnosis results heavily rely on the experience of the technicians. Hence, the automation of cervical cells classification is essential for the development of a computer-aided classification system with low cost, adequate speed, and high accuracy [4], so that researchers and doctors can be released from the boring and repeated routine work. On the other hand, the computer-aided system can reduce the bias and provide robust results. The computer-aided-systems were based on image analysis techniques during the past few decades, which follow a methodological framework with features derived from segmented cytoplasm and nuclei [4]. Based on the experience of skilled technicians and previous computer-aided-diagnosis, hand-crafted features [5; 6], and engineered features [7; 8; 9; 10] are taken as an vital component for the classification task. For example, a typical automated pap-smear analysis system consists of several steps: image acquisition, pre-processing, segmentation, feature extraction, feature selection, and classification [10]. A framework for automated detection and classification of cervical cancer from microscopic biopsy images using biologically interpretable features was proposed in [11]. Several feature selection methods are compared for diagnosis of cervical cancer by using a support vector machine classifier [12]. However, these hand-crafted features and engineered features have some limitations because they may not represent some complex discriminative information, and the feature selection process may have also neglected or excluded some potentially valuable information, which will eventually have some negative effects on the final results. Some other automated pap-smear analysis methods also prompted the development of automated cervical cancer screening, such as fuzzy C-means clustering technique [5], meta-heuristic algorithm [13], and Levenberg-Marquardt with adaptive momentum [14]. Recently, deep learning based methods have addressed many challenging problems in computer vision tasks effectively. Deep learning refers to methods that are designed to automatically learn and obtain high-dimensional features from raw data [15]. The superiority of convolutional neural networks has been demonstrated in plentiful image-processing tasks in the recent years, including object-detection [16; 17], segmentation [18; 19; 20; 21], human pose estimation [22], video classification [23], and image style transfer [24]. Furthermore, CNN models have achieved remarkable achievements in a broad range of diagnose tasks, including identifying moles from melanomas [25; 26], referrals from fundus [27; 28], optical coherence tomography images of the eye [29], breast lesion detection in mammograms [30] and breast cancer classification [31]. For cervical cells classification task, several approaches have been proposed towards the computer-aided systems and have already demonstrated some superior performance compared to the traditional methods. DeepPap was proposed to automatically extract deep features for classification [4]. A five-channel image dataset was formed, and these image patches were fed into the CNN models for classification [32].