Average biased ReLU based CNN descriptor for improved face retrieval

Dubey, Shiv Ram; Chakraborty, Soumendu

doi:10.1007/s11042-020-10269-x

Cited by 31 publications

(14 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The role of activation function is to introduce nonlinear factors to enhance the feature expression ability of the model. At present, the Rectified Linear Unit (ReLU) is the most versatile and effective activation function ( Dubey & Chakraborty, 2021 ), which can improve the network sparsity and reduce the network over-fitting. The expression of ReLU is: …”

Section: Theoretical Methodsmentioning

confidence: 99%

An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism

Wang

2022

PeerJ Computer Science

View full text Add to dashboard Cite

Remaining useful life (RUL) prediction is one of the key technologies of aircraft prognosis and health management (PHM) which could provide better maintenance decisions. In order to improve the accuracy of aircraft engine RUL prediction under real flight conditions and better meet the needs of PHM system, we put forward an improved CNN-LSTM model based on the convolutional block attention module (CBAM). First, the features of aircraft engine operation data are extracted by multi-layer CNN network, and then the attention mechanism is processed by CBAM in channel and spatial dimensions to find key variables related to RUL. Finally, the hidden relationship between features and service time is learned by LSTM and the predicted RUL is output. Experiments were conducted using C-MPASS dataset. Experimental results indicate that our prediction model has feasibility. Compared with other state-of-the-art methods, the RMSE of our method decreased by 17.4%, and the score of the prediction model was improved by 25.9%.

show abstract

Section: Theoretical Methodsmentioning

confidence: 99%

An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism

Wang

2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…In [25], the authors proposed the average biased ReLU (AB-ReLU). Popular CNNs, such as AlexNet and VGGNet, have been used as discriminative feature descriptors in computer vision.…”

Section: Related Workmentioning

confidence: 99%

SinLU: Sinu-Sigmoidal Linear Unit

et al. 2022

View full text Add to dashboard Cite

Non-linear activation functions are integral parts of deep neural architectures. Given the large and complex dataset of a neural network, its computational complexity and approximation capability can differ significantly based on what activation function is used. Parameterizing an activation function with the introduction of learnable parameters generally improves the performance. Herein, a novel activation function called Sinu-sigmoidal Linear Unit (or SinLU) is proposed. SinLU is formulated as SinLU(x)=(x+asinbx)·σ(x), where σ(x) is the sigmoid function. The proposed function incorporates the sine wave, allowing new functionalities over traditional linear unit activations. Two trainable parameters of this function control the participation of the sinusoidal nature in the function, and help to achieve an easily trainable, and fast converging function. The performance of the proposed SinLU is compared against widely used activation functions, such as ReLU, GELU and SiLU. We showed the robustness of the proposed activation function by conducting experiments in a wide array of domains, using multiple types of neural network-based models on some standard datasets. The use of sine wave with trainable parameters results in a better performance of SinLU than commonly used activation functions.

show abstract

“…In comparison to the typical deconvolution layer, this subpixel convolution can decompose low-resolution data into highresolution space without additional computation. Since ReLU has good nonlinear fitting performance [41], it is used as the model's activation function.…”

Section: A Data Collection and Preprocessingmentioning

confidence: 99%

Enhanced Resolution of FY4 Remote Sensing Visible Spectrum Images Utilizing Super-Resolution and Transfer Learning Techniques

Zhang

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Remote sensing images acquired by the FY4 satellite are crucial for regional cloud monitoring and meteorological services. Inspired by the success of deep learning networks in image superresolution, we applied image super-resolution to FY4 visible spectrum (VIS) images. However, training a robust network directly for FY4 VIS image super-resolution remains challenging due to the limited provision of high resolution FY4 sample data. Here, we propose a super-resolution and transfer learning model, FY4-SR-Net. It is composed of pre-training and fine-tuning models. The pre-training model was developed using a deep residual network and a large number of FY4 A 4km and 1km resolution VIS images as the training data. The knowledge derived from 4 km to 1 km resolution images was incorporated into FY4 B 1 km to 0.25 km resolution VIS images. The FY4-SR-Net is finetuned by incorporating limited 1km and 0.25km resolution panchromatic (PAN) images, and then producing 1km superresolution VIS images of the FY4 satellite. Using the one-day FY4 test dataset for qualitative and quantitative evaluations, the FY4-SR-Net outperformed the classic bicubic interpolation approach with a 16.12% reduction in root mean square error (RMSE) and a 2.97% rise in peak signal-to-noise ratio (PSNR) averages. The structural similarity (SSIM) value average increased by 0.0026. This work provides a new precedent for improving the spatial resolution of FY4 series meteorological satellites, which has important scientific significance and application properties.

show abstract

Average biased ReLU based CNN descriptor for improved face retrieval

Cited by 31 publications

References 42 publications

An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism

An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism

SinLU: Sinu-Sigmoidal Linear Unit

Enhanced Resolution of FY4 Remote Sensing Visible Spectrum Images Utilizing Super-Resolution and Transfer Learning Techniques

Contact Info

Product

Resources

About