Unlabeled data application Flashcards
What is Self-Supervised Learning, and how is it used to handle unlabeled data?
It involves pretext tasks (e.g., predicting missing parts of data, denoising, reconstructing from AE) to generate labels from the data itself, allowing models to learn meaningful representations.
What is Contrastive Learning, and how does it work in handling unlabeled data?
Contrastive Learning involves training a model to differentiate between similar and dissimilar data points by pulling representations of similar pairs closer and pushing dissimilar pairs apart in the embedding space.
How does Clustering, such as K-Means or DBSCAN, assist in managing unlabeled data?
Clustering methods group similar data points into clusters, enabling analysis or preprocessing of data without requiring labels.
What is Pseudo-Labeling, and how does it leverage unlabeled data?
Pseudo-labeling involves using a model trained on labeled data to generate labels for unlabeled data, which are then used to refine another model in a semi-supervised manner.
What is semi-supervised learning
Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data to improve model performance (compared to only labeled data). From unlabaleded data comes overall data structure and distribution.
What are Generative Adversarial Networks (GANs), and how can they help with unlabeled data?
GANs consist of a generator and discriminator that learn to generate realistic data, which can be used to augment datasets or create synthetic datasets.
What are Variational Autoencoders (VAEs), and how are they applied to handle unlabeled data?
VAEs learn to encode data into a distribution latent space and then decode it back. Discovering underlying patterns or structures useful for unsupervised tasks and for generating more samples.
How does domain-specific fine-tuning mitigate the lack of pre-trained models?
Domain-specific fine-tuning involves adapting a pre-trained model to a specific domain by training it on a smaller, domain-relevant dataset.
What is Few-Shot Learning, and how does it address lack of data?
Few-shot learning enables models to generalize from a few examples by leveraging prior knowledge or meta-learning techniques.
How does Meta-Learning support the handling of scarce of data?
Meta-learning, or ‘learning to learn,’ trains models to quickly adapt to new tasks using minimal data, enhancing performance in low-resource settings.
What is and why would we use Synthetic Data Generation?
Synthetic data generation creates artificial datasets to train models when real-world data is scarce or unavailable.
How does Transfer Learning from similar domains address a low amount of labeled data in a new domain ?
Transfer learning reuses a model trained on a similar task or domain to improve performance in a new domain.
What role does feature extraction with basic architectures play in handling a low amount of task-specific data?
Basic architectures can be trained to extract general features from raw data, which can then be used to train task-specific models.
