39results about How to "Reduce the amount of model parameters" patented technology

The invention discloses an expansion full-convolution neural network and a construction method thereof. The neural network comprises a convolution neural network, a feature extraction module, and a feature fusion module connected in order; the construction method comprises the following steps: selecting the convolution neural network; removing a full-connection layer and a classification layer forclassifying in the convolution neural network, and only leaving the middle convolution layer and a pooling layer, and extracting a feature map from the convolution layer and the pooling layer; constructing a feature extraction module, wherein the feature extraction module comprises multiple expansion upper-sampling modules connected in series, each expansion upper-sampling module respectively comprises a feature map merge layer, an expansion convolution layer and a deconvolution layer; and constructing a feature fusion module, wherein the feature fusion module comprises a dense expansion convolution block and a deconvolution layer. The expansion full-convolution neural network disclosed by the invention effectively solves the feature extraction and fusion problem in the convolution neuralnetwork, and can be applied to a pixel-level labelling task of an image.

The invention provides a construction method of a fabric defect recognition system based on a lightweight convolutional neural network. The method comprises the following steps: firstly, configuring an operation environment of a fabric defect identification system; obtaining a lightweight convolutional neural network according to factorization convolution; then, collecting fabric image sample data; standardizing the fabric image sample data; dividing the standardized fabric image sample data into a training image set and a test image set; inputting the training image set into a lightweight convolutional neural network for training by using an asynchronous gradient descent training strategy to obtain an LZFNet-Fast model, and finally inputting the test image set into the LZFNet-Fast modelfor testing to verify the performance of the LZFNet-Fast model. According to the method, a standard convolution layer is replaced by a factorized convolution structure, the colored fabric with complextextures is effectively identified, the number of parameters and the calculated amount of the model are reduced, and the identification efficiency is greatly improved.

The invention discloses a text processing model training method, a text method and a device in the field of natural language processing in the field of artificial intelligence. The training method comprises the steps of obtaining a training text; respectively inputting the training text into a teacher model and a student model to obtain sample data output by the teacher model and prediction data output by the student model, the teacher model and the student model respectively comprising an input layer, one or more intermediate layers and an output layer, wherein the sample data comprises sample semantic features output by a middle layer of the teacher model and sample labels output by an output layer, and the prediction data comprises prediction semantic features output by the middle layerof the student model and prediction labels output by the output layer; and training model parameters of the student model based on the sample data and the prediction data to obtain a target student model. According to the technical scheme, knowledge migration is effectively carried out on the student model, so that the accuracy of the text processing result of the student model is improved.

The invention discloses a light human body action recognition method based on deep learning. The method comprises the steps of firstly constructing a light-weight deep learning network (SDNet) combining a shallow network and a deep network, wherein the network comprises a shallow multi-scale module and a deep network module, and constructing a light-weight human body action recognition model basedon deep learning based on the network; in the model, firstly utilizing the SDNet to perform feature extraction and representation on the space-time double flow; utilizing a time pyramid pooling layerto aggregate the video frame level features of the time stream and the space stream into the video level representation; obtaining a recognition result of the space-time double flow to the input sequence via a full connection layer and a softmax layer, and finally fusing the double-flow result in a weighted average fusion mode to obtain a final recognition result. By adopting the light human bodyaction recognition method based on the deep learning, the model parameter quantity can be greatly reduced on the premise of ensuring that the recognition precision is not reduced.

The invention discloses an image defogging method based on a multi-scale dark channel prior cascade deep neural network. The method comprises the following steps: 1, establishing an atomized image training set; 2, defogging a single random foggy image; 3, calculating a loss objective function of the original single foggy image; 4, updating the weight parameter set; 5, calling a new single random foggy image, circulating the step 2 to the step 4 until the loss target function of the original single foggy image is smaller than the loss target function threshold, and determining a final cascade defogging model; and 6, defogging a single actual foggy image. According to the invention, the convolutional neural network is used to estimate dark channel and global illumination parameters on imagesof different scales. The deep neural network is used as a model, and then the dark channel and the defogged image are fused step by step. Finally, the defogged image is obtained through supervised learning. The feature modeling capability of the deep neural network is effectively utilized. The parameter fusion of different scales is achieved. The high-resolution defogged image can be obtained under the condition of few model parameters.

The invention relates to a real-time monocular video depth estimation method, which combines a 2D-CNN (Two-Dimensional Convolutional Neural Network) and a convolutional long-short term memory network to construct a model capable of performing real-time depth estimation on monocular video data by utilizing space and time sequence information at the same time. A GAN (Generative Adversarial Network) is used for constraining an estimated result. In terms of evaluation precision, the method can be compared with a current state-of-th-art model. In the aspect of use overhead, the model operation speed is higher, the model parameter quantity is smaller, and fewer computing resources are needed. The result estimated by the model has good time consistency, and when depth estimation is carried out on multiple continuous frames, the change condition of the obtained depth result image is consistent with the change condition of the input RGB image, so that sudden change and jitter are avoided.

The invention provides a digital string voice processing method and a device thereof. The method includes the following steps: acquiring a target digital string voice; determining a model based on theentire target digital string voice and a pre-established identity vector, determining a target digital identity vector corresponding to each digit in the target digital string voice, and constitutinga digital voiceprint model corresponding to the target digital string voice by all target digital identity vectors, wherein an identity vector determination model is obtained by using training of thedigital string voice, and the digital voiceprint model contains the structured information for training the digital strings in digital string voice. The digital string voice processing method can determine the stable and accurate digital voiceprint model.

The invention provides a translation model training method and device. The translation model comprises an encoder and a decoder. The encoder comprises n encoding layers which are connected in sequence, the decoder comprises n decoding layers which are connected in sequence, a self-attention sub-layer of the ith encoding layer and a self-attention sub-layer of the ith decoding layer share a self-attention parameter, n is greater than or equal to 1, and i is greater than or equal to 1 and less than or equal to n. The method comprises the following steps of: receiving a training statement and a target statement corresponding to the training statement; obtaining a training statement vector corresponding to the training statement and a target statement vector corresponding to the target statement; inputting the training statement vector into the encoder, and performing encoding processing to obtain an encoding vector; inputting the encoding vector and the target statement vector into the decoder, decoding the encoding vector and the target statement vector to obtain a decoding vector, and calculating a loss value according to the decoding vector; and adjusting parameters of the translation model according to the loss value.

The embodiment of the invention relates to a quantification and hardware acceleration method and device for a multi-task neural network. The method comprises steps of obtaining a plurality of preset multi-task neural network models, and enabling the number of layers of the multi-task neural network models to be consistent; extracting a shared weight of the same layer in the plurality of multi-taskneural network models, and generating a corresponding weight code table, the weight code table including the shared weight; training samples of the multiple multi-task neural network models are obtained, shared feature values being analyzed from the training samples, a corresponding feature value code table being generated, and the feature value code table comprising the shared feature values; and putting a plurality of multi-task neural network models into neural network accelerator hardware, and accelerating a plurality of multi-task neural network models based on the weight code table andthe feature value code table.

The invention discloses a scene text recognition method based on HRNet coding and double-branch decoding. When a traditional deep learning method is used for scene text recognition, the recognition accuracy is reduced when the problems of text distortion, image blurring and low resolution are encountered. The method comprises the following steps of: performing random Gaussian blur on an original text image of a single scene to obtain a low-resolution image; and building a scene text recognition model based on HRNet coding and double-branch decoding, wherein the scene text recognition model based on HRNet coding and double-branch decoding comprises a correction network TPS, a coding module, a super-branch branch and a recognition branch. According to the method, HRNet coding and double-branch decoding are introduced, the recognition accuracy of the model for fuzzy and low-resolution images is improved, and the model parameter quantity and time consumption are reduced in the mode of abandoning super branches during testing.

The invention discloses a method and system for identifying Citri medica diseases and insect pests based on an improved yolov5 network, and the method comprises the steps of obtaining a Citri medica diseases and insect pests image, marking, and constructing an initial data set; introducing a yolov5 network model, and improving a backbone network and a Neck module of the yolov5 network model; training, verifying and testing the improved yolov5 network model by using the initial data set to obtain a final diseases and insect pests identification model; pre-processing a to-be-detected image; judging whether the to-be-detected image is shot in a sunny day or a rainy day, and if the to-be-detected image is shot in the rainy day, processing the to-be-detected image by using an Attentive GAN algorithm; if the to-be-detected image is shot in sunny days, not processing; and carrying out diseases and insect pests identification on the pre-processed to-be-detected image based on the diseases and insect pests identification model. According to the method, the improved yolov5 network is combined with the Attentive GAN algorithm, so that the Citri medica diseases and insect pests can be identified under rainy weather conditions, the network parameter quantity and the size of a network model can be reduced, and the identification accuracy is improved.

The invention discloses an urban fine-grained flow prediction method and system based on limited data resources. The method comprises the following steps: acquiring flow distribution data and corresponding external factor data of a to-be-predicted area within a certain time; obtaining a fine granularity flow distribution diagram and a coarse granularity flow distribution diagram according to a set coarse and fine granularity scaling factor; performing down-sampling on the coarse-grained flow distribution diagram to obtain a down-sampled coarse-grained flow diagram; and training a spatial inference encoder from a down-sampling coarse-grained flow diagram to a coarse-grained flow distribution diagram, wherein the spatial inference encoder is used for predicting the fine-grained flow of the region. Under the condition that training resources are limited, an inference network is provided to simplify the urban fine-grained traffic prediction problem, and the traffic prediction precision and reliability of the model are improved.

The invention discloses a neural network design and training method for denoising lightweight real image, the lightweight real image denoising neural network adopts a four-scale U-shaped network structure, and comprises seven second-order residual attention modules, three down-sampling modules and three up-sampling modules. According to the method, the real noisy image can be quickly denoised, and the denoised image is obtained.

The embodiment of the invention provides an optimization method of a stacked one-dimensional convolutional network wake-up acoustic model. The method comprises the following steps: adjusting an expansion coefficient of a time domain convolutional layer in a stacked one-dimensional convolutional network wake-up acoustic model, and increasing a receptive field output by the time domain convolutional layer; and setting an activation function of the time domain convolution layer as a gated linear unit, and combining the gated linear unit with the output of the time domain convolution layer to reduce the dimension of the output of the time domain convolution layer so as to optimize the stacked one-dimensional convolution network wake-up acoustic model. The embodiment of the invention further provides a system for optimizing the stacked one-dimensional convolutional network wake-up acoustic model. According to the embodiment of the invention, the interval of the convolution kernel causes the increase of the receptive field, so that the receptive field of the model is effectively increased, the wake-up precision is improved, meanwhile, after the gating linear unit is combined with the S1DCNN model, the output dimension can be reduced to half of the original dimension, the model parameter quantity is better compressed, and a higher wake-up rate can be achieved under the same parameter quantity.

The invention discloses a keyword identification method and device, a storage medium and computer equipment, and the method comprises the steps: obtaining an original voice signal, and carrying out the framing processing of the original voice signal to obtain a target voice signal, wherein the target voice signal comprises a plurality of voice frames which are arranged according to a time sequence, and the voice frame is represented based on a time domain feature and a frequency domain feature; and inputting the target voice signal into a keyword identification network for keyword identification to obtain a keyword contained in the target voice signal, wherein the keyword identification network comprises a plurality of convolutional network units connected in series and a classifier, the output feature of the previous convolutional network unit is the input feature of the next convolutional network unit, each convolutional network unit comprises one or more convolutional layers, and the classifier is used for classifying the output features of the last convolutional network unit to obtain keywords contained in the target voice signal. The identification precision can be considered while the model parameter quantity is reduced.