Transfer Learning vs. Few-Shot Learning

Transfer Learning

Definition

Transfer learning is a machine learning technique where a model trained on one task is reused as the starting point for a model on a second task. The goal is to leverage knowledge gained from the first task to improve performance on the second task, even when the two tasks are different.

Transfer learning is useful when the target task has limited training data, as the pre-trained model can provide a good starting point and reduce the amount of data needed to train the new model effectively.

Key Characteristics

• Leverages knowledge from a source task to improve performance on a target task
• Reduces the amount of training data needed for the target task
• Can be applied across different domains or tasks, as long as there is some underlying similarity
• Commonly used in computer vision and natural language processing applications (Hu et al., 2022), (Hou et al., 2022)

Few-Shot Learning

Definition

Few-shot learning is a machine learning technique that aims to learn new concepts from only a few training examples. The goal is to enable models to quickly adapt to new tasks or classes with limited data, mimicking the human ability to learn new concepts from just a handful of examples.

Few-shot learning is particularly useful in domains where data is scarce or expensive to obtain, such as medical imaging or robotics. It can also be applied to continual learning, where a model needs to adapt to new tasks over time without forgetting previous knowledge.

Key Characteristics

• Learns new concepts from only a few training examples (e.g., 1-5 samples per class)
• Aims to quickly adapt to new tasks or classes with limited data
• Commonly uses techniques like meta-learning, prototypical networks, and relation networks (Ren & Chen, 2022), (Wang et al., 2022)
• Addresses the challenge of negative transfer, where knowledge from the source task can hinder performance on the target task (Wang et al., 2022), (Oh et al., 2022)

Differences

Data Requirements

• Transfer Learning: Requires a large amount of data for the source task to learn a good initial model, but can then be fine-tuned on the target task with less data.
• Few-Shot Learning: Requires only a few examples (e.g., 1-5 samples per class) for the target task, but relies on meta-learning or other techniques to quickly adapt to the new task.

Learning Approach

• Transfer Learning: Learns a general model on the source task, then fine-tunes or adapts this model to the target task.
• Few-Shot Learning: Learns a meta-learning algorithm that can quickly adapt to new tasks with limited data, often using techniques like prototypical networks or relation networks.

Negative Transfer

• Transfer Learning: Can suffer from negative transfer, where knowledge from the source task hinders performance on the target task. This is often mitigated through careful fine-tuning or adaptation strategies.
• Few-Shot Learning: Specifically addresses the challenge of negative transfer, as the goal is to learn representations and algorithms that can quickly adapt to new tasks without being hindered by previous knowledge. Techniques like re-randomization and subspace methods are used to alleviate negative transfer (Wang et al., 2022), (Oh et al., 2022).