Abstract: Provided are an artificial intelligence (AI)-based method and apparatus for training a classification task model, a device, and a storage medium, which relate to the field of machine learning (ML) technologies. The method includes: training an initial feature extractor by using a first dataset to obtain a feature extractor, the first dataset being a class imbalanced dataset; constructing a generative adversarial network, the generative adversarial network including the feature extractor and an initial feature generator; training the generative adversarial network by using second class samples to obtain a feature generator; constructing a classification task model, the classification task model including the feature generator and the feature extractor; and training the classification task model by using the first dataset, the feature generator being configured to augment the second class samples in a feature space in a training process of the classification task model.

Abstract: A mobile telephone includes an active-pixel sensor in conjunction with a DSP chipset and a light source allowing the telephone to detect horizontal movement across a flat surface. When moved horizontally, the telephone detects relative position movement and transmits a new position to a nearby computer. The computer interprets this new position as a cursor control position and moves its cursor. The telephone is placed into a mode such that physical buttons or displayed buttons on a touch screen operate as a mouse click when pressed. The mouse click is interpreted by the telephone and transmitted to the computer to affect the mouse click. The telephone is also placed into a mode such that raising it from the flat surface indicates that a subsequent three-dimensional movement is interpreted as a mouse click. A shake, rotation or sharp movement is interpreted as a mouse click which is transmitted to the computer to implement the mouse click in conjunction with its cursor.