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A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [ 1 ]
Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern
R-CNN has been extended to perform other computer vision tasks, such as: tracking objects from a drone-mounted camera, [3] locating text in an image, [4] and enabling object detection in Google Lens. [5] Mask R-CNN is also one of seven tasks in the MLPerf Training Benchmark, which is a competition to speed up the training of neural networks. [6]
For computer vision in particular, much progress came from manual feature engineering, such as SIFT features, SURF features, HoG features, bags of visual words, etc. It was a minority position in computer vision that features can be learned directly from data, a position which became dominant after AlexNet. [17]
The deep learning revolution started around CNN- and GPU-based computer vision. Although CNNs trained by backpropagation had been around for decades and GPU implementations of NNs for years, [84] including CNNs, [85] faster implementations of CNNs on GPUs were needed to progress on computer vision. Later, as deep learning becomes widespread ...
ATW CNN architecture. [4] Three CNN streams are used to process spatial RGB images, temporal optical flow images, and temporal warped optical flow images, respectively. An attention model is employed to assign temporal weights between snippets for each stream/modality. Weighted sum is used to fuse predictions from the three streams/modalities.
U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.
[3] [6] Today, however, the CNN architecture is usually trained through backpropagation. This approach is now heavily used in computer vision. [5] [7] In 1969 Fukushima introduced the ReLU (Rectifier Linear Unit) activation function in the context of visual feature extraction in hierarchical neural networks, which he called "analog threshold ...