Deep learning application

1. Computer Vision

(1) Target detection

Object Detection is one of the current research hotspots in the field of computer vision and machine learning. The core task is to screen out all objects of interest in a given image and determine their location and size. One of the difficulties is the pixel-level errors caused by occlusion, illumination, attitude, etc., which is a problem to be challenged and avoided by target detection. Nowadays, in deep learning, DNN is generally used to extract target features, and ROI mapping and IoU are used to determine thresholds and region proposal network RPN unified coordinate regression loss and binary classification loss for joint training.

(2) Semantic Segmentation

Semantic Segmentation (Semantic Segmentation) aims to use objects in images as interpretable semantic categories, which will be obtained by clustering the features learned by DNN. Like target detection, in deep learning, IoU is needed as an evaluation index to evaluate the designed semantic segmentation network. It is worth noting that the semantic categories correspond to different colors, and the generated results need to be compared with the original labeled images to be considered a network that can distinguish different semantic information.

(3) Super-resolution reconstruction

The main task of Super Resolution Construction (Super Resolution Construction) is to reconstruct high-resolution images from observed low-resolution images through software and hardware methods. Such technology is very important in medical imaging and video coding communications. This field is generally divided into single image super-resolution and video super-resolution. Generally, this technology is used to solve problems such as frame loss and frame image blur in video sequences, while in single image, it is mainly to improve details and texture. In deep learning, the residual form network is generally used to learn the accuracy loss caused by bicubic or bicubic downsampling to improve the details of large images; for video super-resolution, optical flow or motion compensation is generally used to solve the frame image reconstruction task.

(4) Pedestrian re-identification

Person re-identification (Person Re-identification), also known as pedestrian re-identification, is a technology that uses computer vision technology to determine whether a specific pedestrian exists in an image or video sequence. It is widely regarded as a subproblem of image retrieval. The core task is to retrieve a cross-device image of a pedestrian given a monitored pedestrian image. Nowadays, general face recognition is combined with this technology to play a role in the assistance of face recognition and when face recognition fails (face is blurred, face is blocked). In deep learning, global and local feature extraction and metric learning are generally used to classify and identify multiple groups of pedestrian pictures.

2. Speech recognition

Speech Recognition (Speech Recognition) is an interdisciplinary subject, which has made remarkable progress in the past ten years. In addition to the need for theoretical knowledge such as digital signal processing, pattern recognition, and probability theory, the development of deep learning has also greatly improved the effect. The purpose of converting sound into bits in deep learning is similar to processing image data in computer vision, converting it into feature vectors. Unlike image processing, it is necessary to sample waves (the form of sound), the way of sampling, and sampling The number and coordinates of points are also key information, and then these digital information are processed and input to the network for training to obtain a model that can perform speech recognition. There are many difficulties in speech recognition, such as overcoming the high similarity of pronunciation syllables for accurate recognition, real-time speech transcription, etc., which requires the voices of many different human samples as data sets to make the deep network have stronger generalization, and the need for Whether the complexity of the designed network itself is appropriate and other conditions.

3. Natural Language Processing

Natural language processing (NLP) is one of the directions in the field of computer science and artificial intelligence. It studies various theories and methods that can realize effective communication between humans and computers in natural language. Due to its nonlinear complex structure, deep learning represents low-dimensional dense and continuous vectors as language units of different granularities, such as words, phrases, sentences, and articles, so that computers can understand the language that participates in weaving through network models, which in turn makes humans communicate with the computer. In addition, researchers in the field of deep learning use neural network models such as loops, convolutions, and recursions to combine different language unit vectors to obtain representations of larger language units. The more complex the combinations of different vector spaces, the more difficult the computer can handle semantic information. Taking human text as input is inherently challenging, so how to process the resulting natural language computer is even more difficult, and this is an area that NLP continues to explore. Through deep learning, people have taken a big step forward in the field of AI. It is believed that the three aspects of "faith, expression, and elegance" in the communication between humans and machines will eventually be realized.