Meta Variance Transfer: Learning to Augment from the Others
Authors: Seong-Jin Park, Seungju Han, Ji-Won Baek, Insoo Kim, Juhwan Song, Hae Beom Lee, Jae-Joon Han, Sung Ju Hwang
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our model on multiple benchmark datasets for few-shot classification and face recognition, on which our model significantly improves the performance of the base model, outperforming relevant baselines. |
| Researcher Affiliation | Collaboration | 1Samsung Advanced Institute of Technology 2KAIST. |
| Pseudocode | Yes | Algorithm 1 Meta Variance Transfer |
| Open Source Code | No | The paper does not contain an explicit statement about providing source code for the described methodology or a link to a public repository. |
| Open Datasets | Yes | We extensively validate our method on diverse few-shot classification datasets. For fine-grained few-shot classification, we use CUB dataset (Wah et al., 2011) and face recognition datasets: VGGface2 (Cao et al., 2018) and CASIA-webface (Yi et al., 2014) datasets. We also use mini Image Net dataset (Deng et al., 2009; Vinyals et al., 2016; Ravi & Larochelle, 2016) which is widely used as a few-shot meta-learning benchmark. |
| Dataset Splits | Yes | We randomly select 300 subjects for each dataset and split them to 200, 50, and 50 subjects for seen, validation, and unseen datasets, respectively. |
| Hardware Specification | Yes | We used a single NVIDIA P40 GPU for training the model. |
| Software Dependencies | No | The paper mentions 'Py Torch implementation' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use 84 84 images for the Conv4 network, and 224 224 images for Res Net-based networks... We train the model by using the Adam optimizer (Kingma & Ba, 2014) for about 110k iterations with the initial learning rate of 0.001, and decay it by 0.1 at 70k and 90k iterations. We set the number of queries during both meta-training and metatesting as 15... For the hyper-parameter λ in equation 1, we used 0.1 throughout the experiments. |