Meta-Learning with Shared Amortized Variational Inference
Authors: Ekaterina Iakovleva, Jakob Verbeek, Karteek Alahari
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on the mini Image Net, CIFAR-FS and FC100 datasets, and present results demonstrating its advantages over previous work. Experiments on few-shot image classification using the mini Image Net, CIFAR-FS and FC100 datasets confirm these findings, and we observe improved accuracy using the variational approach to train the VERSA model (Gordon et al., 2019). |
| Researcher Affiliation | Collaboration | 1Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France. 2Facebook Artificial Intelligence Research, Work done while Jakob Verbeek was at Inria. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | We provide implementaion of our method at: https://github.com/katafeya/samovar. |
| Open Datasets | Yes | Mini Image Net (Vinyals et al., 2016) consists of 100 classes selected from ILSVRC-12 (Russakovsky et al., 2015). FC100 (Oreshkin et al., 2018) was derived from CIFAR-100 (Krizhevsky, 2009). CIFAR-FS (Bertinetto et al., 2019) is another meta-learning dataset derived from CIFAR-100. |
| Dataset Splits | Yes | Mini Image Net... We follow the split from Ravi & Larochelle (2017) with 64 meta-train, 16 meta-validation and 20 meta-test classes, and 600 images in each class. FC100... There are 60 meta-train classes from 12 superclasses, 20 meta-validation, and meta-test classes, each from four corresponding superclasses. CIFAR-FS... It was created by a random split into 64 meta-train, 16 meta-validation and 20 meta-test classes. |
| Hardware Specification | No | The paper does not explicitly mention the specific hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'the code provided by Gordon et al. (2019)' but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For a fair comparison with VERSA (Gordon et al., 2019), we follow the same experimental setup, including the network architectures, optimization procedure, and episode sampling. In particular, we use the shallow CONV-5 feature extractor. In other experiments we use Res Net-12 backbone feature extractor (Oreshkin et al., 2018; Mishra et al., 2018). The cosine classifier is scaled by setting α to 25 when data augmentation is not used, and 50 otherwise. The main and auxiliary tasks are trained concurrently: in episode t out of T, the auxiliary task is sampled with probability ρ = 0.9 12t/T . |