Transfer Learning in Deep Learning

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Questions and Answers

What technique involves modifying a pre-trained model on a new dataset to improve performance?

Feature Extraction

Data Augmentation

Fine-tuning (correct)

Transfer Learning

Which of the following statements is true regarding domain adaptation in transfer learning?

Labeled data is not necessary in the target domain.

It addresses distribution differences between domains. (correct)

Domain adaptation can ignore the data distribution.

Source and target domains must be identical.

What is the primary purpose of using a pre-trained network in transfer learning?

To ensure faster training times without any fine-tuning.

To leverage learned features from a related task. (correct)

To reduce model complexity.

To eliminate the need for labeled data.

Which deep learning method typically requires a significant amount of data for effective training?

Deep Learning Signup and view all the answers

What characterizes the approach to transfer learning for Natural Language Processing (NLP)?

It often involves the use of models trained on large text corpora. Signup and view all the answers

What is a common strategy for implementing transfer learning using VGG-16?

Freezing the lower ConvNet blocks and training new fully-connected layers. Signup and view all the answers

In the context of fine-tuning a ConvNet, what does it mean to fine-tune the weights?

To continue back-propagation to the higher layers of the network. Signup and view all the answers

What is the main goal of transfer learning?

To adapt classifiers learned from a source domain to a new target domain. Signup and view all the answers

What type of classifier can be trained on the CNN codes extracted from images in a VGG16 model?

Linear Support Vector Machine (SVM) or Softmax classifier. Signup and view all the answers

Which part of the VGG-16 architecture is considered a fixed feature extractor during transfer learning?

The convolutional layers. Signup and view all the answers

What type of data is typically used in the pretrained VGG-16 model?

Image data from the ImageNet database. Signup and view all the answers

Which of the following describes the term CNN codes in the context of VGG-16?

Activations of the hidden layer before the classifier, producing a 4096-D vector. Signup and view all the answers

What does adapting classifiers to new domains in transfer learning aim to address?

Differences between the training and test domain characteristics. Signup and view all the answers

What is the primary goal of using gradual unfreezing in the fine-tuning process?

To reduce the problem of catastrophic forgetting. Signup and view all the answers

In the context of transfer learning, which scenario requires using a Support Vector Machine (SVM) or softmax classifier?

When the new dataset is small and very different from the original dataset. Signup and view all the answers

What characteristic defines the ImageNet dataset?

It originally aimed to populate a taxonomy hierarchy. Signup and view all the answers

Which technique combines discriminative fine-tuning, skewed triangular learning rates, and gradual unfreezing?

A combined approach for optimizing performance. Signup and view all the answers

What is a potential downside of overly aggressive fine-tuning?

It may cause the model to forget previously learned information. Signup and view all the answers

Which of the following is a popular pre-trained model commonly used for image classification?

VGG-16 Signup and view all the answers

What is the standard method to adapt a neural network when the new dataset is large and similar to the original dataset?

Perform full fine-tuning of the entire network. Signup and view all the answers

What is the primary focus of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC)?

To evaluate image classification methods on a large dataset. Signup and view all the answers

Study Notes

VGG-16

Developed by the Visual Graphics Group at the University of Oxford
Beat the standard of AlexNet
Quickly adopted by researchers and the industry for image classification tasks

Transfer Learning Strategies

Freeze lower ConvNet blocks as a fixed feature extractor
Treat the rest of the ConvNet as a fixed feature extractor for the new dataset
Compute a 4096-D vector for every image
Train new fully-connected layers
Extract CNN codes for all images
Train a linear classifier (e.g., Linear SVM or Softmax classifier) for the new dataset
Fine-tune the ConvNet
Replace and retrain the classifier on top of the ConvNet on the new dataset
Fine-tune the weights of the pre-trained network by continuing the back-propagation to part of the higher layers

Transfer Learning Use Cases

Spam filtering
Intrusion detection
Sentiment analysis

Transfer Learning Goal

Design learning methods that are aware of the training and test domain difference

Transfer Learning Method

Adapts classifiers learnt from the source domain to the new domain

ImageNet Dataset

Created by professors and researchers at Princeton, Stanford, and UNC Chapel Hill
Initially formed with the goal of populating the WordNet hierarchy
Contains roughly 500-1000 images per concept
Images for each concept were collected by querying search engines and validating on Amazon Mechanical Turk

ILSVRC (ImageNet Large Scale Visual Recognition Challenge)

Most commonly used subset of the ImageNet dataset
Includes:
- 1,281,167 training images
- 50,000 validation images
- 100,000 test images
- 1000 object classes

Applying Transfer Learning to New Datasets

New dataset is small and similar to the original dataset
- Train a linear classifier on the CNN codes
New dataset is large and similar to the original dataset
- Fine-tune through the full network
New dataset is small but very different from the original dataset
- Use an SVM/softmax classifier from activations earlier in the network
New dataset is large and very different from the original dataset
- Fine-tune through the entire network

Fine-Tuning Considerations

Overly aggressive fine-tuning causes catastrophic forgetting
Too cautious fine-tuning leads to slow convergence and overfitting

Proposed Approach: Gradual Unfreezing

Unfreeze the last layer and fine-tune it for one epoch
Unfreeze the next lower frozen layer and fine-tune all unfrozen layers
Repeat until all layers are fine-tuned
Gradual unfreezing leads to better performance

Pre-Trained Models for Image Classification

VGG-16
ResNet50
Inceptionv3
EfficientNet

VGG-16 Model

One of the most popular pre-trained models for image classification
Introduced at the ILSVRC 2014 Conference

Transfer Learning for NLP

Deep Learning methods are data-hungry
Need >50K data items for training
The distributions of the source and target data must be the same
Labeled data in the target domain may be limited
This is typically addressed with transfer learning

Transfer Learning Concepts

Transductive Transfer Learning
Inductive Transfer Learning
Pre-training
Freezing and Fine-tuning
Pre-trained Network

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Description

This quiz explores the concepts and strategies related to transfer learning, particularly using VGG-16 as a foundation. It covers methods for adapting ConvNets for new datasets, practical use cases, and the goal of designing learning methods to account for domain differences. Test your understanding and application of these critical deep learning techniques.