SHENZHEN, China, Feb. 14, 2025 /PRNewswire/ -- MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, they Announced the deep optimization of stacked sparse autoencoders through the DeepSeek open-source model, injecting new vitality into anomaly detection technology and providing an efficient solution.
Data quality is crucial for model performance, so the behavioral data collected in the data preprocessing stage typically contains multiple features with different dimensions and numerical ranges. In order to eliminate the dimensional influence between different features and improve the effectiveness of model training, HOLO uses normalization processing method.
Normalization is a common data preprocessing technique that scales the data to a specific range, typically between 0 and 1 or -1 and 1. By doing so, data from different features can be compared and analyzed on the same scale, avoiding the situation where certain features dominate model training due to their large value ranges. In HOLO's detection project, normalization not only improved the efficiency of model training but also laid a solid foundation for subsequent feature extraction. The data processed through normalization is more aligned with the input requirements of deep learning models, enable the model to learn intrinsic patterns more accurately.
After the data preprocessing is completed, the next step is to input the processed data into the stacked sparse autoencoder model. The stacked sparse autoencoder is a powerful deep learning architecture composed of multiple autoencoder layers, with each layer responsible for extracting features at different levels. HOLO utilizes the DeepSeek model to dynamically adjust the strength and manner of the sparsity constraint, ensuring that the features learned by each layer of the autoencoder are sparse and representative. By appropriately setting the sparsity constraint, the model can better capture key information in the data and reduce redundant features. An autoencoder is an unsupervised learning model designed to encode input data into a lower-dimensional feature representation through the encoder, and then reconstruct the original input data as accurately as possible through the decoder. Between the encoder and decoder, the autoencoder learns the feature representation of the data through a hidden layer.
HOLO has innovated and optimized the stacked sparse autoencoder by utilizing the DeepSeek model. This technique employs a greedy, layer-wise training approach, optimizing the parameters of each autoencoder layer step by step. The core of this layered training strategy is to first train the lower layers of the autoencoder to learn the basic features of the input data, then use the output of the lower-layer autoencoder as the input for the next layer, continuing training and progressively extracting deeper features. In this way, the model is able to gradually capture the complex relationships within the data, enhancing its expressive power. Each layer of the autoencoder is constrained by sparsity, ensuring that the learned features are sparse, meaning that only a few neurons are activated, allowing the model to learn more compact and effective feature representations.
HOLO's stacked sparse autoencoder, trained with the DeepSeek model, adds noise to the input data and requires the model to reconstruct the original input despite the noise interference. This denoising training approach encourages the model to learn more robust feature representations, enabling it to perform accurate anomaly detection even when faced with noisy data in real-world scenarios, thus improving the model's robustness. Specifically, during training, random noise is added to the input data, and the model is tasked with reconstructing the original input. This process forces the model to learn more resilient feature representations, ensuring high accuracy even in the presence of various types of noise in real-world conditions.
In addition to denoising, HOLO also applies Dropout during the training process. Dropout is a commonly used regularization technique primarily aimed at reducing model overfitting. In deep learning models, overfitting refers to the phenomenon where a model performs well on training data but poorly on unseen samples. To avoid this, HOLO randomly drops a subset of neurons during the training of the stacked sparse autoencoder. In each training iteration, the model randomly selects a portion of neurons and sets their outputs to zero. The benefit of this approach is that the model cannot rely on any specific neuron to learn the features of the data, but must instead learn more general and robust feature representations.
In addition, the DeepSeek model utilizes a distributed computing framework, which allocates training tasks across multiple computational nodes for parallel execution. This significantly shortens training time and improves training efficiency. Using the DeepSeek model, pretraining can first be conducted on the stacked sparse autoencoder to learn general feature representations. This pretraining + fine-tuning strategy can greatly accelerate model convergence and improve performance. By introducing the DeepSeek model, HOLO has injected new vitality into optimizing stacked sparse autoencoders. The DeepSeek model provides comprehensive support in areas such as architecture design, training, strategic feature learning, and generalization ability.
About MicroCloud Hologram Inc.
MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud's holographic technology services include high-precision holographic light detection and ranging ("LiDAR") solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems ("ADAS"). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud's holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud's holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/
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