How can you train an image processing model with less labeled data?
Data augmentation
Think of data augmentation as adding spices to a recipe. While it can enhance the flavor, you need the right spices for the dish. In medical imaging, it's similar. It is crucial to apply these transformations accurately, especially in domain-specific contexts such as medical imaging.
For example, if you are training a model to detect abnormalities in X-rays, standard transformations may not accurately replicate real-world variations in patient data. Instead, it would help if you considered making adjustments like modifying brightness or simulating various imaging devices.
Additionally, in sensitive domains like healthcare, ethical considerations come into play, where it is important to balance model enhancement with sensitive information.
Semi-supervised learning
Did you know that with semi-supervised learning, a company can identify fraudulent transactions in a pool of 10 million user transactions with only 5% of the data labeled as fraudulent or not fraudulent?
It's a powerful technique that can save companies millions of dollars and prevent financial fraud. This approach is highly useful as it enables the entire dataset to be analyzed without the need for a large team of annotators or a decrease in accuracy.
Furthermore, this method can also be employed in a wide range of applications such as webpage classification, speech analysis, and named-entity recognition, where labeling data is difficult and requires domain expertise. These are particularly advantageous where labeled data are scarce.
Transfer learning
Transfer learning is a technique that involves using a pre-trained model as a starting point for a beginner. However, it can be challenging when the high-level features of the pre-trained model do not adequately differentiate between categories in the new task.
For instance, the pre-trained model may be able to recognize doors but may not be able to distinguish between their open & closed status. Another challenge is when working with dissimilar datasets. In such cases, initializing the new model with pre-trained weights can be helpful.
However, trimming layers to simplify the model may lead to overfitting, which can be detrimental to the model's performance. Hence, it's important to balance pre-trained features with specific new tasks.
Active learning
Overcoming the challenges of active learning for unlabelled data involves a strategic approach to selecting annotated samples. With the abundance of unlabelled data and the high cost of obtaining labels, it's crucial to choose which instances to annotate carefully.
To ensure the selected subsample is non-redundant and accurately represents the underlying distribution, ML teams need purpose-built tools. These tools should enable the visualization of assets, model predictions, and ground truths, allowing the identification of model predictions and evaluation of performance on both training and production data.
Analyzing model confidence scores, ensuring reproducibility, and comparing models throughout iterative data-centric cycles is crucial.
Weakly supervised learning
Ever wondered if machines could learn without constant human guidance? Weakly supervised learning is the answer!
These refer to the effectiveness of image and text classification models that can train themselves without requiring extensive human feedback. This novel approach sidesteps the need for copious labeled data, making it a boon for companies navigating various contexts with minimal user input.
Yet, with its perks come trade-offs; weakly supervised learning may not match the precision of supervised counterparts and demands more training time. Despite these drawbacks, it excels in scenarios where limited labeled data meets the demand for accurate predictions.
