Data Augmentation

A Cutting-Edge Review of Diffusion Models and Knowledge Graphs

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A Cutting-Edge Review of Diffusion Models and Knowledge Graphs

Approximately 7500 words, suggested reading time 16 minutes. This article introduces a new knowledge graph diffusion model named DiffKG, which combines generative diffusion models with data augmentation paradigms to achieve robust knowledge graph representation learning. The importance of knowledge graphs (graph networks) in recommendation systems is self-evident, but not all relationships are relevant to the … Read more

Audio Augmentation in TensorFlow and PyTorch

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Audio Augmentation in TensorFlow and PyTorch

Source: Deephub Imba This article is approximately 2100 words long and is suggested to be read in 9 minutes. This article will introduce two methods to apply augmentation to datasets in TensorFlow. For image-related tasks, common data augmentation methods include rotating, blurring, or resizing images. This is because the inherent properties of images make data … Read more

10 TensorFlow 2.x Tips for Efficient Usage

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10 TensorFlow 2.x Tips for Efficient Usage

Click on the above “Beginner Learning Vision”, choose to add Star or Top ” Important content delivered at the first time Author | Rohan Jagtap Compiled by | ronghuaiyang Source | AI Park TensorFlow 2.x provides a lot of simplicity in building models and the overall use of TensorFlow. In this article, we will explore … Read more

13 Image Augmentation Methods in Pytorch

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13 Image Augmentation Methods in Pytorch

Using data augmentation techniques can increase the diversity of images in the dataset, thereby improving the performance and generalization ability of the model. The main image augmentation techniques include: Resizing Grayscale Transformation Normalization Random Rotation Center Cropping Random Cropping Gaussian Blur Brightness and Contrast Adjustment Horizontal Flip Vertical Flip Gaussian Noise Random Blocks Central Region … Read more

Using GANs for Data Augmentation

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Using GANs for Data Augmentation

Follow the WeChat public account “ML_NLP“ Set as “Starred“, to receive heavy content promptly! Reprinted from: AI Park Author: Sam Nolen Translation: ronghuaiyang Introduction Applicable in cases with very few samples. Even imperfect synthetic data can improve classifier performance. Generative Adversarial Networks (GANs) were introduced by Ian Goodfellow in 2014 and have become a very … Read more

Data Augmentation and Prediction of Food Processing Contaminants

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Data Augmentation and Prediction of Food Processing Contaminants

The accurate prediction of contaminants in the food processing process is of great significance for food safety. However, due to the complexity of food processing technology and the difficulty in detecting contaminants, the amount of data is relatively small, making it difficult to meet the requirements for modeling and prediction. Therefore, it is necessary to … Read more

Practical Summary of CNN Tuning

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Practical Summary of CNN Tuning

Click on the above “Beginner Learning Vision“, select to add “Star” or “Top“ Essential insights delivered promptly Reprinted from: Author | Charlotte Source | Deep Learning Enthusiasts Editor | Jishi Platform Summary of tuning techniques, all about CNN optimization. Summary of CNN Optimization Systematic evaluation of CNN advances on the ImageNet Using ELU non-linearity without … Read more

Training Convolutional Neural Networks (CNN) From Scratch Using Data Augmentation

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Training Convolutional Neural Networks (CNN) From Scratch Using Data Augmentation

Click on "XiaoBai Learns Vision" above, choose to add "Star" or "Top" Heavy content delivered at the first time Introduction This article aims to address overfitting in neural networks. Overfitting will be your main concern as you train the model with only 2000 data samples.There are some methods to help overcome overfitting, namely dropout and … Read more

Comprehensive Overview of Data Augmentation Techniques in Computer Vision

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Comprehensive Overview of Data Augmentation Techniques in Computer Vision

Click the “CVer” above and select “Star” to pin it Heavyweight content delivered first If we were to rank several stages in the deep learning development process by importance, preparing training data would surely be among the top few. It’s important to understand that once a model network is written, it is merely a chunk … Read more

Kaggle Champions Share: Image Recognition and Classification Competition

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Kaggle Champions Share: Image Recognition and Classification Competition

1 Compiled by New Intelligence Source: blog.kaggle.com Compiled by: Jia Yuepeng [New Intelligence Guide]The champion team of the Kaggle Ocean Fish Recognition and Classification Competition shares their technology: How to design robust optimization algorithms? How to analyze data and perform data augmentation? Technical details include using images from different boats for validation and how to … Read more