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..
Efficient K-Shot Learning With Regularized Deep Networks
Authors: Donghyun Yoo, Haoqi Fan, Vishnu Boddeti, Kris Kitani
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our method can be easily applied to several popular convolutional neural networks and improve upon other state-of-the-art fine-tuning based k-shot learning strategies by more than 10% of accuracy. |
| Researcher Affiliation | Collaboration | 1The Robotics Institute, School of Computer Science, Carnegie Mellon University 2Facebook 3Michigan State University |
| Pseudocode | Yes | Algorithm 1: Grouping and average gradient update algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code, nor does it state that the code is available in supplementary materials or via a specific repository link. |
| Open Datasets | Yes | Our pre-trained network is the Res Net-18 architecture by (He et al. 2016) trained on the Image Net dataset. For this task, we consider the Office dataset introduced by (Saenko et al. 2010). Our pre-trained network is the Res Net-18 architecture trained on the CIFAR-100 dataset while the k-shot learning task is classification on the CIFAR-10 dataset. |
| Dataset Splits | Yes | Aft is the accuracy of the fine-tuned network of which parameters are clustered and calculated on the validation set. The k-shot data are chosen randomly from the target training set for fine-tuning and we evaluate on the entire target test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For fine-tuning, the learning-rate is 0.01, and it is changed to 0.001 after 1000 iteration. |