publish [GPU]: course_UvA-DL/12-meta-learning

Author: Azure Pipelines

.notebooks/course_UvA-DL/12-meta-learning.ipynb

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.notebooks/course_UvA-DL/12-meta-learning.jpg

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+title: 'Tutorial 12: Meta-Learning - Learning to Learn'
+author: Phillip Lippe
+created: 2021-08-21
+updated: 2023-03-14
+license: CC BY-SA
+tags:
+- Few-shot-learning
+- MAML
+- ProtoNet
+description: 'In this tutorial, we will discuss algorithms that learn models which
+  can quickly adapt to new classes and/or tasks with few samples.
+
+  This area of machine learning is called _Meta-Learning_ aiming at "learning to learn".
+
+  Learning from very few examples is a natural task for humans. In contrast to current
+  deep learning models, we need to see only a few examples of a police car or firetruck
+  to recognize them in daily traffic.
+
+  This is crucial ability since in real-world application, it is rarely the case that
+  the data stays static and does not change over time.
+
+  For example, an object detection system for mobile phones trained on data from 2000
+  will have troubles detecting today''s common mobile phones, and thus, needs to adapt
+  to new data without excessive label effort.
+
+  The optimization techniques we have discussed so far struggle with this because
+  they only aim at obtaining a good performance on a test set that had similar data.
+
+  However, what if the test set has classes that we do not have in the training set?
+
+  Or what if we want to test the model on a completely different task?
+
+  We will discuss and implement three common Meta-Learning algorithms for such situations.
+
+  This notebook is part of a lecture series on Deep Learning at the University of
+  Amsterdam.
+
+  The full list of tutorials can be found at https://uvadlc-notebooks.rtfd.io.
+
+  '
+requirements:
+- torchvision
+- matplotlib
+- seaborn
+- lightning>=2.0.0
+- tensorboard
+- scipy
+accelerator:
+- CPU
+- GPU
+environment:
+- matplotlib==3.8.4
+- numpy==1.26.4
+- ipython==8.16.1
+- torch==2.0.1
+- torchmetrics==1.2.1
+- torchvision==0.15.2
+- lightning==2.3.3
+- tensorboard==2.17.0
+- seaborn==0.13.2
+- urllib3==2.2.2
+- pytorch-lightning==2.0.9.post0
+- setuptools==69.0.3
+- scipy==1.14.0
+published: '2024-07-19T20:22:41.980859'