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Knee OsteoArthritis (KOA) is a prevalent musculoskeletal condition thatimpairs the mobility of senior citizens. The lack of sufficient data in themedical field is always a challenge for training a learning model due to thehigh cost of labelling. At present, Deep neural network training stronglydepends on data augmentation to improve the model's generalization capabilityand avoid over-fitting. However, existing data augmentation operations, such asrotation, gamma correction, etc., are designed based on the original data,which does not substantially increase the data diversity. In this paper, wepropose a learning model based on the convolutional Auto-Encoder and a hybridloss strategy to generate new data for early KOA (KL-0 vs KL-2) diagnosis. Fourhidden layers are designed among the encoder and decoder, which represent thekey and unrelated features of each input, respectively. Then, two key featurevectors are exchanged to obtain the generated images. To do this, a hybrid lossfunction is derived using different loss functions with optimized weights tosupervise the reconstruction and key-exchange learning. Experimental resultsshow that the generated data are valid as they can significantly improve themodel's classification performance.

Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee OsteoArthritis Classification