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https://github.com/oscarknagg/few-shot
Repository for few-shot learning machine learning projects
https://github.com/oscarknagg/few-shot
few-shot-learning machine-learning maml meta-learning miniimagenet omniglot pytorch research
Last synced: 14 days ago
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Repository for few-shot learning machine learning projects
- Host: GitHub
- URL: https://github.com/oscarknagg/few-shot
- Owner: oscarknagg
- License: mit
- Created: 2018-10-30T23:24:51.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-11-25T04:18:31.000Z (almost 5 years ago)
- Last Synced: 2024-10-15T10:05:09.768Z (29 days ago)
- Topics: few-shot-learning, machine-learning, maml, meta-learning, miniimagenet, omniglot, pytorch, research
- Language: Python
- Size: 16.1 MB
- Stars: 1,202
- Watchers: 14
- Forks: 245
- Open Issues: 36
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-machine-learning-resources - **[Code Collection - shot?style=social) (Table of Contents)
README
# Few-shot learning
The aim for this repository is to contain clean, readable and tested
code to reproduce few-shot learning research.
This project is written in python 3.6 and Pytorch and assumes you have
a GPU.
See these Medium articles for some more information
1. [Theory and concepts](https://towardsdatascience.com/advances-in-few-shot-learning-a-guided-tour-36bc10a68b77)
2. [Discussion of implementation details](https://towardsdatascience.com/advances-in-few-shot-learning-reproducing-results-in-pytorch-aba70dee541d)
# Setup
### Requirements
Listed in `requirements.txt`. Install with `pip install -r
requirements.txt` preferably in a virtualenv.
### Data
Edit the `DATA_PATH` variable in `config.py` to the location where
you store the Omniglot and miniImagenet datasets.
After acquiring the
data and running the setup scripts your folder structure should look
like
```
DATA_PATH/
Omniglot/
images_background/
images_evaluation/
miniImageNet/
images_background/
images_evaluation/
```
**Omniglot** dataset. Download from https://github.com/brendenlake/omniglot/tree/master/python,
place the extracted files into `DATA_PATH/Omniglot_Raw` and run
`scripts/prepare_omniglot.py`
**miniImageNet** dataset. Download files from
https://drive.google.com/file/d/0B3Irx3uQNoBMQ1FlNXJsZUdYWEE/view,
place in `data/miniImageNet/images` and run `scripts/prepare_mini_imagenet.py`
### Tests (optional)
After adding the datasets run `pytest` in the root directory to run
all tests.
# Results
The file `experiments/experiments.txt` contains the hyperparameters I
used to obtain the results given below.
### Prototypical Networks
Run `experiments/proto_nets.py` to reproduce results from [Prototpyical
Networks for Few-shot Learning](https://arxiv.org/pdf/1703.05175.pdf)
(Snell et al).
**Arguments**
- dataset: {'omniglot', 'miniImageNet'}. Whether to use the Omniglot
or miniImagenet dataset
- distance: {'l2', 'cosine'}. Which distance metric to use
- n-train: Support samples per class for training tasks
- n-test: Support samples per class for validation tasks
- k-train: Number of classes in training tasks
- k-test: Number of classes in validation tasks
- q-train: Query samples per class for training tasks
- q-test: Query samples per class for validation tasks
| | Omniglot | | | |
|------------------|----------|-----|------|------|
| **k-way** | **5** |**5**|**20**|**20**|
| **n-shot** | **1** |**5**|**1** |**5** |
| Published | 98.8 |99.7 |96.0 |98.9 |
| This Repo | 98.2 |99.4 |95.8 |98.6 |
| | miniImageNet| |
|------------------|-------------|-----|
| **k-way** | **5** |**5**|
| **n-shot** | **1** |**5**|
| Published | 49.4 |68.2 |
| This Repo | 48.0 |66.2 |
### Matching Networks
A differentiable nearest neighbours classifier.
Run `experiments/matching_nets.py` to reproduce results from [Matching
Networks for One Shot Learning](https://arxiv.org/pdf/1606.04080.pdf)
(Vinyals et al).
**Arguments**
- dataset: {'omniglot', 'miniImageNet'}. Whether to use the Omniglot
or miniImagenet dataset
- distance: {'l2', 'cosine'}. Which distance metric to use
- n-train: Support samples per class for training tasks
- n-test: Support samples per class for validation tasks
- k-train: Number of classes in training tasks
- k-test: Number of classes in validation tasks
- q-train: Query samples per class for training tasks
- q-test: Query samples per class for validation tasks
- fce: Whether (True) or not (False) to use full context embeddings (FCE)
- lstm-layers: Number of LSTM layers to use in the support set
FCE
- unrolling-steps: Number of unrolling steps to use when calculating FCE
of the query sample
I had trouble reproducing the results of this paper using the cosine
distance metric as I found the converge to be slow and final performance
dependent on the random initialisation. However I was able to reproduce
(and slightly exceed) the results of this paper using the l2 distance
metric.
| | Omniglot| | | |
|---------------------|---------|-----|------|------|
| **k-way** | **5** |**5**|**20**|**20**|
| **n-shot** | **1** |**5**|**1** |**5** |
| Published (cosine) | 98.1 |98.9 |93.8 |98.5 |
| This Repo (cosine) | 92.0 |93.2 |75.6 |77.8 |
| This Repo (l2) | 98.3 |99.8 |92.8 |97.8 |
| | miniImageNet| |
|------------------------|-------------|-----|
| **k-way** | **5** |**5**|
| **n-shot** | **1** |**5**|
| Published (cosine, FCE)| 44.2 |57.0 |
| This Repo (cosine, FCE)| 42.8 |53.6 |
| This Repo (l2) | 46.0 |58.4 |
### Model-Agnostic Meta-Learning (MAML)
I used max pooling instead of strided convolutions in order to be
consistent with the other papers. The miniImageNet experiments using
2nd order MAML took me over a day to run.
Run `experiments/maml.py` to reproduce results from [Model-Agnostic
Meta-Learning](https://arxiv.org/pdf/1703.03400.pdf)
(Finn et al).
**Arguments**
- dataset: {'omniglot', 'miniImageNet'}. Whether to use the Omniglot
or miniImagenet dataset
- distance: {'l2', 'cosine'}. Which distance metric to use
- n: Support samples per class for few-shot tasks
- k: Number of classes in training tasks
- q: Query samples per class for training tasks
- inner-train-steps: Number of inner-loop updates to perform on training
tasks
- inner-val-steps: Number of inner-loop updates to perform on validation
tasks
- inner-lr: Learning rate to use for inner-loop updates
- meta-lr: Learning rate to use when updating the meta-learner weights
- meta-batch-size: Number of tasks per meta-batch
- order: Whether to use 1st or 2nd order MAML
- epochs: Number of training epochs
- epoch-len: Meta-batches per epoch
- eval-batches: Number of meta-batches to use when evaluating the model
after each epoch
NB: For MAML n, k and q are fixed between train and test. You may need
to adjust meta-batch-size to fit your GPU. 2nd order MAML uses a _lot_
more memory.
| | Omniglot | | | |
|------------------|----------|-----|------|------|
| **k-way** | **5** |**5**|**20**|**20**|
| **n-shot** | **1** |**5**|**1** |**5** |
| Published | 98.7 |99.9 |95.8 |98.9 |
| This Repo (1) | 95.5 |99.5 |92.2 |97.7 |
| This Repo (2) | 98.1 |99.8 |91.6 |95.9 |
| | miniImageNet| |
|------------------|-------------|-----|
| **k-way** | **5** |**5**|
| **n-shot** | **1** |**5**|
| Published | 48.1 |63.2 |
| This Repo (1) | 46.4 |63.3 |
| This Repo (2) | 47.5 |64.7 |
Number in brackets indicates 1st or 2nd order MAML.