| Summary: | This study focuses on the crucial aspect of lossless compression for FAST pulsar search data. The deep generative model PixelCNN, stacking multiple masked convolutional layers, achieves neural network autoregressive modeling, making it one of the most excellent image density estimators. However, the local nature of convolutional networks causes PixelCNN to concentrate only on nearby information, neglecting important information at greater distances. Although deepening the network can broaden the receptive field, excessive depth can compromise model stability, leading to issues like gradient degradation. To address these challenges, this study combines causal attention modules with residual connections, proposing a residual causal attention module to enhance the PixelCNN model. This innovation not only resolves convergence problems arising from network deepening but also expands the receptive field. It facilitates the extraction of crucial image details while capturing the global structural information of the image, significantly enhancing the modeling capabilities for pulsar data. In the experiments, the model is trained and validated using the HTRU1 dataset. This study compares the average negative log-likelihood score with baseline models like the GMM, STM, and PixelCNN. The results demonstrate the superior performance of our model over other models. Finally, this study introduces the practical compression encoding process by combining the proposed model with arithmetic coding.
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