## 1 Introduction  
A rolling bearing is the core component in many rotating machines. Any failure in rolling bearings can lead to a chain reaction of faults in the whole mechanical system, causing the rapid and unexpected breakdown of the machine. Being an essential part of condition monitoring, the early and accurate identification of an imminent failure is deemed effective in reducing property loss and even possible casualties caused by catastrophic industrial breakdowns. Recent years have seen the rapid development of innovative artificial intelligence (AI) algorithms, including two major groups: machine learning (ML) and deep learning (DL). Inspired by the progress in this field on the one hand, and informed by the long-standing unaddressed challenges faced by traditional 'feature-based' approaches, practitioners strive to find new solvers--intelligent methods capable of detecting the emerging faults early, reliably and seamlessly, without heavy reliance on human labor and expert experience [1; 2]. To date, numerous intelligent fault diagnosis schemes have been proposed. [PERSON] et al. have analyzed the relevant bibliometric data in this field [2]. The application of DL started to thrive after 2015 and it gradually surpassed traditional ML models. At present, the DL-based machine fault diagnosis framework has become the mainstream of intelligent fault diagnostics. Compared with traditional ML models, the procedures of feature extraction and fault recognition are integrated within the DL approach. The DL approach is unique in that it is capable of extracting features automatically from the input data through multiple layers comprising processing units called hidden neurons. This makes DL suitable to process the raw signal straightforwardly, without any signal pre-processing. Classical DL architectures include the Back Propagation Neuron Network (BPNN) [3], Convolutional Neuron Network (CNN) [4; 5], Deep Boltzmann Machine (DBM), Deep Brief Network (DBN), Autoencoder (AEN) [6; 7], Long Short-Term Memory (LSTM) network [8; 9] and their variants. The emerging damage in the machine can be communicated through different signal sources. Among them, the vibration-based technique is the most widely used one owing to its simplicity, the transparency of the analysis based on spectral features and cost advantages. However, there are shortcomings of vibration signals, which are to be mentioned. Firstly, the vibration acceleration signals can hardly be detectable until the damage develops significantly to a mature stage, corresponding to large-scale faults causing vibrations in heavy or slowly rotating structures. It is often too late to use this information for preventive maintenance [10]. Secondly, the vibration signals induced by early defects are easily masked by the routine background mechanical vibration of the rotating machine. It has been frequently reported that acoustic emission (AE) signals can detect the incipient crack earlier than traditional vibration signals [11; 12; 13; 14]. Even if this claim is not always justified, the modern AE technique provides a promising means for EFD in roller bearings. The AE is referred to as a phenomenon of transient elastic wave generation by a sudden local drop in internal stress within the material. Compared to the vibration signal, AE has a much wider frequency range (20 kHz to 10 MHz), and, thus, it does not overlap or interfere with low-frequency mechanical vibration signals. To date, the 'feature-based' parametric analysis prevails in interpreting information derived from AE waveforms [15; 16; 17]. However, these hand-designed features are inherently linked through AE to the specific signal processing techniques in the time, frequency or time-frequency domains [15; 18; 19]. These features need to be carefully extracted and analyzed by experts, and there is no guarantee that the features tailored to a specific fault diagnosis condition are applicable for other tasks.