Prototypical Network

Paper Prototypical Networks for Few-shot Learning.

Overview [Abstract]

This paper propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. This paper provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning. The authors further extend prototypical networks to zero-shot learning and achieve state-of-theart results on the CU-Birds dataset.

Results

Citation

@inproceedings{snell2017prototypical,
  title={Prototypical Networks for Few-shot Learning},
  author={Snell, Jake and Swersky, Kevin and Zemel, Richard},
  booktitle={Advances in Neural Information Processing Systems},
  year={2017}
 }

Training a prototypical network

Install dependencies

• This code has been tested on Ubuntu 16.04 with Python 3.6 and PyTorch 0.4.
• Install PyTorch and torchvision.
• Install torchnet by running pip install git+https://github.com/pytorch/tnt.git@master.
• Install the protonets package by running python setup.py install or python setup.py develop.

Set up the Omniglot dataset

• Run sh download_omniglot.sh.

Train the model

• Run python scripts/train/few_shot/run_train.py. This will run training and place the results into results.
• You can specify a different output directory by passing in the option --log.exp_dir EXP_DIR, where EXP_DIR is your desired output directory.
• If you are running on a GPU you can pass in the option --data.cuda.
• Re-run in trainval mode python scripts/train/few_shot/run_trainval.py. This will save your model into results/trainval by default.

Evaluate

• Run evaluation as: python scripts/predict/few_shot/run_eval.py --model.model_path results/trainval/best_model.pt.