Few-Shot Learning with Graph Neural Networks
Authors: Victor Garcia Satorras, Joan Bruna Estrach
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate experimentally the model on few-shot image classification, matching state-of-the-art performance with considerably fewer parameters, and demonstrate applications to semi-supervised and active learning setups. ... 6 EXPERIMENTS For the few-shot, semi-supervised and active learning experiments we used the Omniglot dataset presented by Lake et al. (2015) and Mini-Imagenet dataset introduced by Vinyals et al. (2016) which is a small version of ILSVRC-12 Krizhevsky et al. (2012). |
| Researcher Affiliation | Academia | Victor Garcia Amsterdam Machine Learning Lab University of Amsterdam Amsterdam, 1098 XH, NL v.garciasatorras@uva.nl Joan Bruna Courant Institute of Mathematical Sciences New York University New York City, NY, 10010, USA bruna@cims.nyu.edu |
| Pseudocode | No | The paper describes methods and architectures but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code available at: https://github.com/vgsatorras/few-shot-gnn |
| Open Datasets | Yes | For the few-shot, semi-supervised and active learning experiments we used the Omniglot dataset presented by Lake et al. (2015) and Mini-Imagenet dataset introduced by Vinyals et al. (2016)... |
| Dataset Splits | Yes | We used the splits proposed by Ravi & Larochelle (2016) of 64 classes for training, 16 for validation and 20 for testing. Using 64 classes for training, and the 16 validation classes only for early stopping and parameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper describes the model architecture and data augmentation, but it lacks specific numerical hyperparameters such as learning rate, batch size, number of epochs, or detailed optimizer settings for reproducibility. |