#Maml meta learning full#
#Maml meta learning series#

# Path to the folder where the datasets are/should be downloaded (e.g. reset_orig () # Import tensorboard % load_ext tensorboard set_matplotlib_formats ( 'svg', 'pdf' ) # For export matplotlib. set_cmap ( 'cividis' ) % matplotlib inline Import json import os import random import urllib.request from collections import defaultdict from copy import deepcopy from statistics import mean, stdev from urllib.error import HTTPError import lightning as L import matplotlib import matplotlib.pyplot as plt import matplotlib_inline.backend_inline import numpy as np import seaborn as sns import torch import torch.nn.functional as F import torch.optim as optim import as data import torchvision from import LearningRateMonitor, ModelCheckpoint from PIL import Image from torchvision import transforms from torchvision.datasets import CIFAR100, SVHN from to import tqdm plt. We will again be using PyTorch Lightning. Here, we would not only have novel classes, but also a completely different dataset.įirst of all, let’s start with importing our standard libraries. For few-shot classification, we consider a task to distinguish between novel classes. Notebook can be easily applied to these settings since we only use a different definition of a ‘task’. In NLP, we can think of a model which was trained sentiment classification, hatespeech detection and sarcasm classification, to adapt to classifying the emotion of a text. For example, an robot agent that learned to run, jump and pick up boxes, should quickly adapt to collecting and stacking boxes. For instance, we would train the model on the binary classifications of cats-birds and flowers-bikes, but during test time, the model would need to learn from 4 examples each the difference between dogs and otters, two classes we have not seen during training (Figure credit - Lilian Weng).Ī different setup, which is very common in Reinforcement Learning and recently Natural Language Processing, is to aim at few-shot learning of a completely new task. We will focus on the task of few-shot classification where the training and test set have distinct Meta-Learning offers solutions to these situations, and we will discuss three popular algorithms: Prototypical Networks ( Snell et al., 2017), Model-Agnostic Meta-Learning / MAML ( Finn et al., 2017), and Proto-MAML ( Triantafillou et al., 2020). This notebook requires some packages besides pytorch-lightning. Give us a ⭐ on Github | Check out the documentation | Join us on Slack Setup ¶

#Maml meta learning full#

The full list of tutorials can be found at.

#Maml meta learning series#

This notebook is part of a lecture series on Deep Learning at the University of Amsterdam. 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.

However, what if the test set has classes that we do not have 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. 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. This is crucial ability since in real-world application, it is rarely theĬase that the data stays static and does not change over time. 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. Learning from very few examples is a natural task for humans. This area of machine learning is called Meta-Learning aiming at “learning to learn”. In this tutorial, we will discuss algorithms that learn models which can quickly adapt to new classes and/or tasks with few samples. **MAML**, or **Model-Agnostic Meta-Learning**, is a model and task-agnostic algorithm for meta-learning that trains a model’s parameters such that a small number of gradient updates will lead to fast learning on a new task.Tutorial 12: Meta-Learning - Learning to Learn ¶