Transfer Learning: Leveraging Knowledge for Better Machine Learning

Introduction of Transfer Learning

Transfer learning is a machine learning technique that allows you to reuse knowledge gained from a previous model. Instead of creating and training a new model from scratch for a related problem, you use a pre-trained model as a starting point. You take the pre-trained model, which was trained on a large dataset for a similar but different task, and transfer the weights and knowledge to a new model. Then you fine-tune the new model with a small dataset specific to your new task. This method leverages the existing knowledge from the original model to boost the new model’s performance – especially when you only have limited data for the new task. It requires less data, resources, and training time than building a model from scratch. Transfer learning is used widely in computer vision, natural language processing, and speech recognition to improve performance and efficiency.

Fundamental uses of Transfer Learning

Transfer learning leverages knowledge from large, pre-trained models to boost the performance of specialized models. For image classification, we can fine-tune models pre-trained on ImageNet to classify narrow sets of images more efficiently. For instance, using a pre-trained ImageNet model for flower classification requires less data and training time than building a model from scratch. Similarly, for natural language tasks like sentiment analysis, utilizing pre-trained word embeddings like GloVe as a starting point provides the model with learned word representations that improve performance.

In this blog, we will examine several widely used pre-trained architectures, such as VGG and Inception, all of which are trained on the ImageNet dataset and can be implemented through popular frameworks such as TensorFlow, Keras, and Pytorch.”

ImageNet Dataset Description

The ImageNet dataset is a vast collection of annotated photographs primarily used for computer vision research, containing approximately 14 million images, over 21,000 classes or groups, and more than one million images with bounding box annotations. The ImageNet large scale visual recognition Challenge (ILSVRC) (Russakovsky et al., 2015) is a well-known challenge in deep learning that uses this dataset. The challenge aims to develop a model that can accurately classify images into 1000 separate object categories.

In image classification, the ImageNet challenge serves as a benchmark standard for evaluating the performance of various computer vision-based algorithms. During this challenge, CNN and deep learning techniques dominate the leaderboard.

Pre-trained CNN models

There are two popular models that we can consider. These models can be employed for various tasks, including image generation, neural style transfer, image classification, image captioning, and anomaly detection. The two models are:

• VGG Model
• Inceptionv3 (GoogLeNet)

VGG Model

VGG is a convolutional neural network consisting of 19 layers, developed and trained by Karen Simonyan and Andrew Zisserman at the University of Oxford in 2014. You can find more information about this network in their paper titled “Very Deep Convolutional Networks for Large-Scale Image Recognition,” published in 2015. (Simonyan and Zisserman, 2015)

The VGG-19 model was trained using over one million images from the ImageNet database and comes with ImageNet trained weights that you can import. With this pre-trained network, you can classify up to 1000 objects. The network was trained on 224×224 pixel color images.

Inceptionv3 (GoogLeNet)

Inceptionv3 is a convolutional neural network with 50 layers, developed and trained by Google. You can find detailed information about this network in the “Going Deeper with Convolutions” paper. The pre-trained version of Inceptionv3 with ImageNet weights can classify up to 1000 objects. Compared to VGG19, the input image size for this network was larger at 299×299 pixels. In 2014’s ImageNet competition, Inceptionv3 outperformed VGG19 to take the top spot.(Szegedy et al., n.d.)

Conclusion

With easy access to state-of-the-art neural network models, attempting to create our model with limited resources can be akin to reinventing the wheel. Therefore, it’s more beneficial to work with these pre-trained models by adding a few new layers on top that are tailored to our specific computer vision task and then training these models. This approach is more likely to yield successful results than building a model from scratch.

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