TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
Authors: Sung Whan Yoon, Jun Seo, Jaekyun Moon
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When tested on the Omniglot, mini Image Net and tiered Image Net datasets, we obtain state of the art classification accuracies under various few-shot scenarios. |
| Researcher Affiliation | Academia | 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. |
| Pseudocode | Yes | Algorithm 1 Episodic learning is done by NE episodes. |
| Open Source Code | Yes | Codes are available on https://github.com/istarjun/Tap Net |
| Open Datasets | Yes | Omniglot (Lake et al., 2015); mini Image Net (Vinyals et al., 2016); tiered Image Net (Ren et al., 2018) |
| Dataset Splits | Yes | For our experiment, we have used 84 84 downsized color images with a split of 64 training classes, 16 validation classes and 20 test classes. [...] These categories are split into 20 training, 6 validation and 8 test categories, and the training, validation and test sets contain 351, 97 and 160 classes, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | The Adam optimizer (Kingma & Ba, 2014) with an optimized learning-rate decay is employed. For all experiments, the initial learning rate is 10 3. In the 20-way Omniglot experiment, the learning rate is reduced by half at every 4.0 104 episodes, but for 5-way mini Image Net and 5-way tiered Image Net classification, we cut the learning rate by a factor of 10 at every 2.0 104 and 4.0 104 episodes, respectively, for 1-shot experiments and every 4.0 104 and 3.0 104 episodes, respectively, for 5-shot experiments. |