Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
Authors: Sung Whan Yoon, Jun Seo, Jaekyun Moon
ICML 2019 | Venue PDF | 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. |