How can you use transfer learning to improve ANN training?

Here are some steps to use transfer learning to improve artificial neural network (ANN) training: ? Choose a pre-trained model that is relevant to your problem. ? Decide which layers and weights you want to reuse from the pre-trained model. ? Add some new layers on top of the pre-trained model to customize it for your problem. ? Train the new model on your dataset. ? Optionally, you can fine-tune the entire model or part of it by unfreezing some or all of the pre-trained layers and re-training them with a very low learning rate. This can help you adapt the pre-trained features to your new data and achieve better results

To choose a suitable model for your task, start by selecting a pre-trained model that has been trained on a diverse dataset like ImageNet or CIFAR-10, which captures general features and patterns. Many pre-trained models are available online on platforms like TensorFlow Hub or PyTorch Hub. Ensure the chosen model aligns with your input data format, desired output type, and task complexity. For instance, if you're classifying animal images, opt for a model trained on ImageNet, which encompasses a wide range of animal categories.

Pretrained Models: Start with a pre-trained neural network (e.g., a well-trained CNN for image tasks) and remove the output layer. Feature Extraction: Use the feature extraction layers to extract relevant features from your dataset and add new layers for your specific task, fine-tuning the model. Regularization: Transfer learning helps by leveraging knowledge from a related task, speeding up training, and often improving generalization, reducing the need for large labeled datasets.

Transfer learning can improve ANN training by: Reusing a pre-trained model to train a new model on a similar task Reducing training times and improving performance Reducing the risk of overfitting To use transfer learning, choose a pre-trained model, adapt it to your target task, and fine-tune it on your target data.

In the second step, you need to adapt the architecture of the pre-trained model to suit your specific task. This involves making adjustments like removing, adding, or modifying layers within the model. Typically, the last layer, often a fully connected one generating the final output, is removed and substituted with a new layer tailored to your desired output. For example, if your goal is to classify animal images into 10 categories, you would replace the last layer with a new layer comprising 10 neurons and a softmax activation function.

After selecting a suitable pre-trained model for your task, its existing architecture may not align with your intent. Most useful open-source image classifiers are trained on thousands of target categories, yet you may only be interested in a few (e.g., animal types). Layer addition is a common solution, but other methods include (1) Pruning by modifying/removing layers to reduce complexity for simpler tasks, (2) Neuron fusion/fission, which alters connections to adjust complexity, and (3) Dynamic adaptation, which optimizes architecture based on training/validation performance. PyTorch, TensorFlow enable low-level manipulation, while huggingface and FastAI provide higher-level methods for adaptation.

As Peyman discussed, depending on the results you get, you can also 'unfreeze' the model, and retrain the inner layers. As you gain more coding expertise, you can play more with the layers, such as modifying the number of neurons of the each layer, changing the non-linear functions between the linear layers etc. If you want to see good results without getting too deep into coding, FastAI is a great library. It's built on top of Pytorch(the most used Deep learning network for experimenting) & makes getting started easier. Seeing results at the beginning of your Deep Learning journey will motivate you to keep going, but you can always learn more and dive deep into the library. I 'd also like to recommend the accompanying FastAI course.

We used transfer learning to train a clinical NLP model. We trained the model (fine-tuning) on clinical and biomedical data, using weights from a pre-trained model in generalist Portuguese language as a checkpoint. Therefore, our model performed better in clinical texts.

When fine-tuning the model, using a suitable learning rate is vital. Learning rate(LR) is a type of 'hyperparameter', which will determine how large or small the steps your model take, in order to minimize the error. Some deep learning libraries use a 'learning rate finder' function, where the computer tries training your model with different learning rates(starting with the smallest), and calculating the error(the loss function) at each cycle of training. Then it can be plotted in a graph, and we can use the graph to find the learning rate at which the loss is minimal. So, rather than blindly experimenting, we can focus our training to a narrow range of values for the LR, thus speeding up the training process.

In the third step, fine-tuning the pre-trained model's parameters is crucial to optimize its performance for your specific task. Achieve this by training the model on your new data, utilizing a small learning rate, and selecting an appropriate optimizer. You can also opt to freeze certain layers, keeping their weights constant while updating only the weights of the new or modified layers. This strategy helps prevent overfitting and retains valuable features learned by the pre-trained model. For instance, if using a model trained on ImageNet, consider freezing the initial convolutional layers (for low-level features) and fine-tuning the latter layers (for high-level features).

3 basic but important points to be added from my experience as : Data Augmentation (for vision tasks): If you're working on a vision task, using data augmentation which involves making slight modifications to the training images (e.g., rotations, translations, zooming) to increase the effective size of the training set and improve generalization. Use a Lower Learning Rate for Fine-tuning: When fine-tuning the pre-trained layers, it's often a good practice to use a lower learning rate compared to the rate used for the new layers. Regularization: To prevent overfitting, especially if you're working with a smaller dataset, use regularization techniques like dropout, L1/L2 regularization, or early stopping.

In the final step, evaluate the results of your transfer learning process by assessing metrics like accuracy and loss on your new data, typically using a validation or test set. You can also compare these results to those obtained with a baseline model, which is trained from scratch on your new data without any pre-trained weights. This comparison helps you gauge the advantages and constraints of transfer learning for your specific task. For instance, when classifying animal images using transfer learning, you should anticipate improved accuracy and lower loss, especially if your dataset is small or similar to the pre-trained model's data.

3 mistakes I have done with Transfer learning are: 1. Assuming Transfer Learning is Always the Best Option: In some cases, especially when the new dataset is large and very different from the pre-trained dataset, it might be better to train a model from scratch. 2.Neglecting Regular Evaluation: It's essential to continuously monitor the performance on a validation set. Without regular checks, it's easy to miss signs of overfitting or underfitting. 3.Not Freezing the Right Layers: Sometimes, freezing only the initial layers and fine-tuning the deeper ones might be more effective, especially when the new task is different but not entirely unrelated to the original task. Deciding which layers to freeze or fine-tune is critical.

Transfer learning is a valuable technique for enhancing the training of artificial neural networks (ANNs) more efficiently. It entails leveraging the knowledge and weights from a pre-trained model when tackling a new task that shares similarities with the original one. This article outlines four key steps in using transfer learning to enhance ANN training: selecting an appropriate model, customizing the architecture, fine-tuning parameters, and assessing the outcomes.

One niche approach for fine tuning which is "Gradual or scheduled unfreeze" :- Gradual Unfreezing: Instead of unfreezing all layers at once, consider gradually unfreezing layers starting from the top (the layers closer to the output). As the top layers of deep networks are generally more task-specific, they might need more adjustment than the lower layers which capture more generic features. For example, begin by training only the newly added layers while keeping the pre-trained layers frozen. Then, progressively unfreeze and fine-tune the top layers of the pre-trained model. Depending on the size of your dataset you can decide to eventually unfreeze more layers or even the entire model. You need custom codes for this not API's.