471 Result(s)
within
English
Computer Science
Image Processing and Computer Vision
Yi**ang Chen
Chapter
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Chapter and Conference Paper
Towards Real-Time High-Definition Image Snow Removal: Efficient Pyramid Network with Asymmetrical Encoder-Decoder Architecture
In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation varies from image to image. Recent methods adopt deep neural networks to ...
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Chapter and Conference Paper
Ethical Concerns of COVID-19 Contact Tracing: A Narrative Review
Contact tracing has been widely adopted during COVID-19 to curb the spread of infection. Despite its effectiveness, ethical issues abound and many people are not willing to use it. Toward understanding the eth...
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Chapter
Instance Weighting Methods
Instance weighting methods are one of the most effective methods for transfer learning. Technically speaking, any weighting methods can be used for evaluating the importance of each instance. In this chapter, ...
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Chapter
Geometrical Feature Transformation Methods
In this chapter, we introduce the geometrical feature transformation methods for transfer learning, which is different from statistical feature transformation in the last section. The geometrical features can ...
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Chapter
Pre-Training and Fine-Tuning
In this chapter, we focus on modern parameter-based methods: the pre-training and fine-tuning approach. We will also step into deep transfer learning starting from this chapter. In next chapters, the deep tran...
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Chapter
Transfer Learning for Computer Vision
Today, most of the deep learning algorithms, tutorials, and talks are using computer vision tasks as benchmarks. For instance, the common “Hello world” example of deep learning tutorial is MNIST digits classif...
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Chapter
Transfer Learning for Speech Recognition
Speech recognition is also an important research area of transfer learning. Speech recognition has several scenarios: cross-domain ASR and cross-lingual ASR. In this chapter, we introduce how to implement thes...
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Chapter
Federated Learning for Personalized Healthcare
Federated learning aims at building machine learning models without compromising data privacy from the clients. Since different clients naturally have different data distributions (i.e., the non-i.i.d. issue),...
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Chapter and Conference Paper
MCSketch: An Accurate Sketch for Heavy Flow Detection and Heavy Flow Frequency Estimation
Accurately finding heavy flows in data streams is challenging owing to limited memory availability. Prior algorithms have focused on accuracy in heavy flow detection but cannot provide the frequency of a heavy...
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Chapter
Overview of Transfer Learning Algorithms
This chapter gives an overview of transfer learning algorithms so that readers can learn and understand detailed algorithms in other chapters with a thorough view. To facilitate such an understanding, we estab...
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Chapter and Conference Paper
Object Centric Point Sets Feature Learning with Matrix Decomposition
A representation matching the invariance/equivariance characteristics must be learnt to rebuild a morphable 3D model from a single picture input. However, present approaches for dealing with 3D point clouds de...
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Chapter
Statistical Feature Transformation Methods
In this chapter, we introduce statistical feature transformation methods for transfer learning. This kind of approaches is extremely popular in existing literature with good results. Especially, they are often...
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Chapter and Conference Paper
Fake News Detection Based on the Correlation Extension of Multimodal Information
Online social media is characterized by a large number of users that creates conditions for large-scale news generation. News in multimodal form (images and text) often has a serious negative impact. Existing ...
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Chapter and Conference Paper
Unsupervised Deep Transfer Learning Model for Tool Wear States Recognition
Heavy worn tools can cause severe cutting vibrations, leading to a decrease in the surface quality of the workpiece. It is important to monitor tool states and replace the worn tool in time. The traditional to...
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Chapter
Theory, Evaluation, and Model Selection
We have introduced several basic algorithms for transfer learning. However, we did not show how to select models and tune hyperparameters, which will be covered in this chapter. Moreover, we will also introduc...
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Chapter and Conference Paper
UnconFuse: Avatar Reconstruction from Unconstrained Images
The report proposes an effective solution about 3D human body reconstruction from multiple unconstrained frames for ECCV 2022 WCPA Challenge: From Face, Body and Fashion to 3D Virtual avatars I (track1: Multi-...
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Chapter
Deep Transfer Learning
With the development of deep learning, more and more researchers adopt deep neural networks for transfer learning. Compared to traditional machine learning, deep transfer learning increases the performance on ...
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Chapter
Adversarial Transfer Learning
Generative Adversarial Nets (GAN) is one of the most popular research topics in recent years. In this chapter, we introduce adversarial transfer learning methods, which belongs to the implicit feature transfor...
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Chapter
Transfer Learning for Natural Language Processing
Recent years have witnessed the fast development of natural language processing (NLP). Particularly, the pre-training technique has been playing a key role in common NLP tasks. In this chapter, we show how to ...
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Chapter
Concluding Remarks
Transfer learning is extremely important to solve the label scarce situations and non-i.i.d issues in machine learning. In this book, we start from the basics of machine learning to the concepts of transfer le...
