BxD Primer Series: Transfer Learning Techniques

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Welcome to BxD Primer Series where we are covering topics such as Machine learning models, Neural Nets, GPT, Ensemble models, Hyper-automation in ‘one-post-one-topic’ format. Today’s post is on?Transfer Learning Techniques. Let’s get started:

The What:

Transfer learning involves using pre-trained models as a starting point for new related tasks. It allows developers to take advantage of the knowledge and learning captured by large datasets and neural networks, and apply it to new problems that have limited data.

Different types of transfer learning techniques can be used depending on the nature of new problem and available resources. Common techniques are (more details later):

1. Feature extraction?involves using a pre-trained neural network to extract relevant features from input data. These features can then be fed into a new model, which is trained to perform a specific task.
2. Fine-tuning?involves taking a pre-trained model and adapting it to a new task by re-training some or all of its layers with a new dataset.
3. Domain adaptation?is used to transfer knowledge from a source domain to a target domain, where the data have different characteristics or distributions.
4. Multi-task learning?involves training a single model to perform multiple tasks simultaneously. The model learns shared representations that can be useful for different tasks.
5. One-shot learning?involves training a model to recognize new objects or patterns from only one or a few examples.

Applications of Transfer Learning:

Some examples where transfer learning have worked successfully:

1. To improve accuracy of?image classification models, pre-trained models such as VGG, ResNet, and Inception are available.
2. To not only classifying objects but also localizing them within an image (object detection), pre-trained models such as Faster R-CNN, SSD, and YOLO are available.
3. To improve accuracy of?face recognition?systems, pre-trained models such as VGGFace and FaceNet are available.
4. To improve performance on?natural language processing, pre-trained models such as BERT, GPT, and ELMO are available.
5. To improve accuracy of?speech recognition?tasks, pre-trained models such as DeepSpeech and Wav2Vec are available.

And many more…

Feature Extraction in Transfer Learning:

The process of feature extraction involves taking a pre-trained neural network and?removing the output layer?that was trained for original task. Remaining layers in the network can then be used as a?fixed feature extractor that can map?input data to a set of high-level features that capture important patterns in data.

The output of this feature extractor is then?fed into a new model, that is trained to perform a different task on new data. The advantage here is that pre-trained network has already learned to recognize important features in data, and this knowledge is leveraged to improve performance of new model. This requires significantly less data for new model and is computationally efficient.

Fine-tuning in Transfer Learning:

Fine-tuning involves taking a pre-trained model and adapting it to a new task by?re-training some or all of its layers?with a new dataset. It is used when pre-trained model needs to be adapted to a new task different from the one model was originally trained on.

Fine-tuning drastically reduces the amount of labeled data required for training and speeds up the training process by initializing the model with pre-trained weights.

Common methods for fine-tuning a pre-trained model:

1. Freeze early layers: In general, the early layers of a pre-trained model are responsible for detecting low-level features such as edges and corners. Since these features are likely to be useful for many different tasks, it is often a good idea to freeze the early layers and only fine-tune the later layers that are more task-specific.
2. Gradual unfreezing?involves first fine-tuning last few layers, then gradually unfreezing and fine-tuning more layers as needed. This is a good balance between retaining v/s acquiring knowledge.
3. Layer-wise learning rate adjustment:?Layers closer to output require a lower learning rate to prevent overfitting, while lower layers require a higher learning rate to learn new features.
4. Curriculum learning?involves gradually increasing the complexity of training data during fine-tuning. The model learn more complex features and prevent overfitting on simpler data.

Note: Main difference between feature extraction and fine-tuning is the degree of adaptation of pre-trained model to new task.

• Feature extraction reuses pre-trained model's weights?without updating them, and only the new model's weights are trained on new task.
• Fine-tuning adapts the pre-trained model's weights to new task.
• Feature extraction is often used when new task is significantly different from original task. For example, a pre-trained model for image classification may be used for feature extraction in a new model for object detection.
• Fine-tuning is often used when new task is similar to original task, but with some differences. For example, a pre-trained model for image classification may be fine-tuned on a new dataset with similar categories but different image styles or resolutions.

Domain Adaptation in Transfer Learning:

Domain adaptation is the process of adapting a pre-trained model to a new domain with different data distributions, without the need to retrain the model from scratch on new data.

For example, a model trained to recognize faces in images captured by high-quality camera may not perform well when applied to low-quality images captured by a surveillance camera. In this case, domain adaptation can be used to adapt the pre-trained model to new domain of low-quality images.

Data distributions alignment, style transfer (from source to target) and learning domain-invariant representations are common approaches in domain adaptation.

Multi-task Transfer Learning:

Multi-task learning is to train a single model to perform multiple tasks simultaneously. The model learns shared representations that can be useful for different tasks.

This is done by training the model on a?joint objective function?that combines the loss functions of all tasks. Shared layers of model learn to extract features that are useful for all tasks, while task-specific layers learn to perform specific tasks.

For example, in computer vision application, a multi-task learning model could be trained for object detection and image segmentation simultaneously. Shared layers of model learn to extract relevant features from input images, while task-specific layers learn to predict locations and labels of objects in image, as well as segment different regions of image.

• Task Relatedness: If the tasks are too dissimilar, the common knowledge will not be relevant.
• Training on multiple tasks simultaneously can result in?interference between tasks, where model's performance on one task degrade due to conflicting information from another task.

One/Few-Shot Transfer Learning:

One/Few-shot learning involves training a model to recognize new objects or patterns from only one or a few examples, rather than the hundreds or thousands of examples typically required for traditional machine learning tasks.

For example, it could be used to quickly recognize new products in a retail setting, or to identify new types of tumors in medical imaging even in cases of few examples.

In one-shot learning, pre-trained model is typically used as a feature extractor. These features can then be used to train a new model that recognize new objects from only a few examples.

One approach is to use a siamese network, which consists of two identical neural networks that share same weights. First network is trained on a set of examples of a particular class, while second network is trained on a different set of examples?of same class. During training, the two networks are fed pairs of examples, and the objective is to?learn a similarity metric?that can distinguish between pairs of examples from same class and different classes. Trained model is used to recognize new examples from only one or few examples.

Overcoming Catastrophic Forgetting:

Catastrophic forgetting is a phenomenon where a neural network trained on one task tends to?forget its existing knowledge when learning a new task. This can occur when the network is fine-tuned on a new dataset or when new layers are added to the network.

Techniques to avoid catastrophic forgetting:

• Use gradual unfreezing or layer freezing, where fine-tuning the network is a stepwise process, starting with output layers and gradually moving towards input layers.
• Use weight consolidation, which aim to preserve the importance of weights learned during pre-training phase. This technique use regularization to maintain previous knowledge while allowing for new knowledge to be learned.
• Use knowledge distillation, which involves using a pre-trained model as a teacher to train a new model as a student. The teacher model transfers its knowledge to student model, which is trained on new task.

Note: Negative transfer is one more challenge in transfer learning which occurs when the pre-trained model is not well-suited for target task and actually hinders performance. This usually occurs when there are significant differences between source and target tasks.

The Why:

Reasons to use transfer learning techniques:

1. Can significantly reduce time and resources required for training new models from scratch.
2. Pre-trained models already contain learned features and representations, which leads to improved performance and accuracy compared to training from scratch.
3. Pre-trained models generalize better to unseen data, as it have learned features that are generally applicable to many different tasks.
4. Works with smaller amounts of labeled data.
5. Pre-trained models are often robust to noise and outliers in new dataset.

The Why Not:

Reasons to not use transfer learning techniques:

1. Overfitting can occur in transfer learning if the new dataset is?significantly different?from the original pre-training dataset.
2. Pre-trained model may have been trained on datasets with specific biases or assumptions, which may not apply to new dataset.
3. Pre-trained model may not be suitable for new dataset due to differences in domain or distribution.
4. Pre-trained models may have limited flexibility in terms of architecture and design.

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In next edition, we will wrap up the primer series.

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