Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Authors: Siavash Khodadadeh, Sharare Zehtabian, Saeed Vahidian, Weijia Wang, Bill Lin, Ladislau Boloni
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested the proposed algorithms on three few-shot learning benchmarks: (a) the 5-way Omniglot (Lake et al. (2011)), a benchmark for few-shot handwritten character recognition, (b) the 5-way Celeb A few-shot identity recognition, and (c) the Celeb A attributes dataset (Liu et al. (2015)) proposed as a few-shot learning benchmark by (Hsu et al. (2019)). |
| Researcher Affiliation | Academia | 1 Dept. of Computer Science University of Central Florida 2 Dept. of Electrical & Computer Engineering University of California San Diego |
| Pseudocode | Yes | Algorithm 1: LASIUM for unsupervised meta-learning task generation |
| Open Source Code | Yes | Our source code is also available on Github 2. Footnote 2: https://github.com/siavash-khodadadeh/Meta Learning-TF2.0 |
| Open Datasets | Yes | We tested the proposed algorithms on three few-shot learning benchmarks: (a) the 5-way Omniglot (Lake et al. (2011)), a benchmark for few-shot handwritten character recognition, (b) the 5-way Celeb A few-shot identity recognition, and (c) the Celeb A attributes dataset (Liu et al. (2015)) proposed as a few-shot learning benchmark by (Hsu et al. (2019)). |
| Dataset Splits | Yes | During meta-learning, an additional set ,D(val) T , is attached to each task that contains another N K(val) data points separate from the ones in D(tr) T . We have exactly K(val) samples for each class in D(val) T as well. We set K(val) to be 15. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper describes neural network architectures and models used but does not provide specific software dependencies with version numbers (e.g., Python version, library versions like TensorFlow, PyTorch, or CUDA versions). |
| Experiment Setup | Yes | In this section, we report the hyperparameters of LASIUM-MAML in Table 5 and LASIUM-Proto Nets in Table 6 for Omniglot, Celeb A, Celeb A attributes and Mini-Image Net datasets. Table 5: LASIUM-MAML hyperparameters summary |