Standard 6 stops to get here · leads to 6

Transfer Learning

Leveraging knowledge learned from one task/domain to improve performance on a related task with less data.

Your route here

6 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  3. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  4. Unsupervised Learning ✓ understood

    Learning from unlabeled data to discover hidden patterns, structures, or relationships without explicit target outputs.

  5. Self-Supervised Learning ✓ understood

    Learning representations from unlabeled data by creating supervised tasks from the data itself (masked prediction, contrastive learning).

  6. Pre-training ✓ understood

    Training a model on a large dataset (often self-supervised) before fine-tuning on specific tasks, enabling transfer learning.

  7. Transfer Learning · you are here ✓ understood

Where it sits

Before this

Pre-training
Transfer Learning

Leads to

Foundation Model Transfer Learning in Vision Zero-Shot Learning Meta-Learning Multi-Task Learning Sim-to-Real Transfer

Explore nearby

Training Fine-Tuning The process of further training a pre-trained model on a specific dataset to adapt it for a particular task or domain. Vision & Multimodal Transfer Learning in Vision Using pre-trained vision models (ImageNet) as feature extractors or fine-tuning for specific visual tasks. Language & LLMs Foundation Model Large pre-trained models serving as a base for various downstream tasks (GPT, BERT, CLIP, SAM). Language & LLMs Few-Shot Learning Learning to perform a task from a small number of examples provided in the prompt, without parameter updates. Training Catastrophic Forgetting The tendency of neural networks to completely forget previously learned information when learning new tasks.