What are the best practices for transfer learning?

In my experience, if you can get a foundation/base model that’s trained on same data distribution as the real data from domain - more than half the job is done. Your model will have learnt many of the patterns already and you only need to fine-tune it on specific new content.

The importance of the actual training data cannot be overstated. It's crucial to remember that the effectiveness of a source model, even one as robust as one trained on ImageNet, largely depends on the relevance and quality of your specific training data. If the data doesn't closely align with your target task, the benefits of the pre-trained model diminish. Therefore, it's essential to curate a dataset that is representative, diverse, and aligned with the unique requirements of your domain. This ensures that the transfer learning process adapts to the specifics of your task, leading to more accurate and effective outcomes. In transfer learning, the right training data is just as important as the right source model.

I think As a fresher one should Gain a solid understanding of pre-trained models and their architectures to choose the most suitable one for your task. Learn how to fine-tune pre-trained models by updating only specific layers or parameters relevant to your specific dataset, saving time and resources. Implement data augmentation techniques to artificially increase the diversity of your training data, enhancing the model's ability to generalize to new data and improve performance. Leveraging and learning the new pre trained models can make a positive immediate impact on your skills and way of writing the clean code.

Transfer learning in machine learning involves selecting a pre-trained model relevant to your task, customizing it by choosing specific layers, tuning hyperparameters to optimize learning, and evaluating its performance. It's crucial to carefully adjust settings like learning rates and batch sizes, as well as utilize techniques such as data augmentation and regularization to enhance model effectiveness. Additionally, consider domain adaptation if your source and target domains differ significantly. These practices ensure efficient and effective utilization of transfer learning for improved model performance.

Transfer learning highly depends on the source model pretrained on a large dataset. The type of dataset the source model is trained on impacts the results the model would yield for the target domain or task at hand. For instance, a task like Object Detection would require a source model that's pretrained on a large dataset of diverse object classes like COCO dataset, CIFAR dataset or MNIST for handwritten digit detection. Finetuning layers of the network by freezing the pretrained model's original weights and adding new weights in the retraining phase would significantly accentuate the performance of the model. Thus, Transfer Learning is a useful technique for tuning the model to perform target downstream tasks at hand.

One of the most important best practices is to carefully analyze the similarity between the original training data and your target dataset. If they are very similar, you can likely freeze more lower-level layers. Another good practice is to start with a simple transfer learning approach, like frozen lower layers, and incrementally unfreeze more layers if performance is insufficient. It's easy to over-engineer at first. Finally, leverage pre-trained models whenever possible, even if not state-of-the-art. The generalization abilities they've learned can significantly jump start your modeling process compared to training a full model from scratch.

Since the source model is usually trained on a generic data which can help the model learn generic features, there is usually no need of training all layers. We therefore train last few layers of the network to finetune on target domain. Number of layers to train should be decided on three factors: 1) Number of data points in the target domain 2) Number of classes 3) Similarity of source and target domain The more samples and classes we have, the more layers we should train. The more similar our data to the target is, the lesser layers we should train.

Selecting the appropriate layers in transfer learning is a nuanced decision balancing your data size and task specificity. For smaller or similar datasets, freeze the lower layers of the source model to retain general features, and fine-tune or add new layers at the top for task-specific learning. Conversely, with larger or more distinct datasets, consider fine-tuning more or all layers to fully adapt the model to the new domain. This step is crucial for effectively leveraging the pre-trained model while ensuring it's tailored to your specific task.

The appropriate number of layers to adjust or fine-tune in transfer learning is determined by several factors, including the similarity between the source task (the task on which the model was originally trained) and the target task (the new task), the size and diversity of the dataset, and the model's complexity. In the context of Experimentation and Validation: - It is best practice that fine-tuning should start with just the last few layers. Based on the performance in validation, the number of layers fine-tuned can then be gradually increased. - The model’s performance should be monitored on a validation set to determine whether the fine-tuning of additional layers leads to improved results.

Layer selection is one of the most potent and influential steps in the transfer learning method. As the higher layers capture the task dependent and data specific features, it is generally advised not to add regularisation techniques like dropout, batch normalisation etc as we may not learn the data completely for our model. Also unfreezing the lower layers would lead to overfitting due to increase in no of learning parameters which also may not provide any sufficient influence on the model. We need to also ensure whether to use transfer learning or not in some cases as most of the SOTA models are pre-trained on common data while our data may not fall into the domain. Eg: Using VGG, Resnet for skew detection may not be fruitful.

Hyperparameter tuning for transfer learning models can be tricky since we aren't training a model from scratch. The pre-trained weights encode a lot of generalized knowledge we want to retain, so we have to tread carefully. As a rule of thumb, I start with very small learning rates, like 1/10th of what I'd normally use for a randomly initialized model. Additionally, extensive regularization may not be needed or even counterproductive if using lower frozen layers. The general gist is to lean on the power of transfer, not fight it with hyper aggressive tuning to eke out every drop of performance. Start conservatively, then scale up carefully as warranted.

One of the highest risks of fine-tuning, especially with complex models with lots of world knowledge, is catastrophic forgetting. If the problem shows up, I always suggest to include rehearsal strategies during fine-tuning to make sure the model does not forget all the important bits from the source task.

One thing to add up here is that it's very important to follow up the adjustment of the hyperparameter with cross validation so that we can select the best optimisation choice for the tuning.

Fine-tuning hyperparameters is akin to adjusting a camera for the perfect shot. Hyperparameters, acting like exposure and focus settings, influence your model's learning. Envision your model as a photographer adjusting to different lighting conditions (hyperparameters). In transfer learning, adapt these settings based on data size, complexity, and uniqueness. A smaller learning rate is like patiently focusing on crucial details, while a larger batch size is akin to averaging out variations for a clearer image. Discovering ideal hyperparameters is like tweaking camera settings for the best shot. Experiment with combinations using methods like grid or random search – finding the perfect adjustments to capture your data's essence.

In the process of adjusting hyperparameters for transfer learning, the key is to start with a much smaller learning rate. This approach is critical because the target model, due to its pre-training, is already positioned in a favorable area of the loss landscape, likely near a local minimum. Using a high learning rate risks overshooting the model's weights out of this advantageous zone which can potentially be similar to resetting all its weights with random initializer. A smaller learning rate, therefore, is crucial for fine-tuning the model without disrupting its position in the loss landscape and losing the valuable knowledge it has acquired.

Choose wisely: Select a pre-trained model with similar data and tasks to yours, maximizing knowledge transfer. Freeze and finesse: Initially "freeze" layers in the pre-trained model, training only your custom layers to avoid overfitting. Tune with care: Use smaller learning rates for frozen layers and adjust them gradually for optimal performance. Data matters: Ensure your data aligns with the pre-trained model's domain and augment it strategically if needed. Monitor and adapt: Track progress closely, analyze errors, and refine your approach to achieve the best results. Don't overfit: Be mindful of data size and complexity to avoid memorizing your specific dataset instead of generalizing for new data.

I think that if you spend 10 hours on ML, then you should spend at least 7 hours working on data processing. I cannot stress how important clean and quality data means to your model. It does not have to be massive, like 10 million rows, but it has to cover the necessary corner cases, lack of noise, and challenging enough to the model so that it can learn properly.

In short, transfer learning is a very powerful technique contributing to the efficiency, performance, and adaptability of ML models across a wide range of applications. However, remember to follow the best practices to tune well, freeze layers, don't overfit, and track progress closely.

Remember, it's not just about lifting weights; it's about smart lifting. Consider the complexity of your task and adjust the model complexity accordingly. Overkill with too complex a model and you're just burning computational calories.

Domain Shift and Adaptation Challenges Domain Discrepancies: When the source and target domains differ significantly, transfer learning might face challenges in adapting the learned features effectively. For instance, transferring knowledge from natural images to medical images might encounter domain-specific variations in features and distributions. Catastrophic Forgetting: Overwriting valuable learned representations from the source domain can occur if not carefully managed during fine-tuning. Strategies like gradual unfreezing of layers or selective learning rate adjustments help mitigate this issue.