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..
EventDrop: Data Augmentation for Event-based Learning
Authors: Fuqiang Gu, Weicong Sng, Xuke Hu, Fangwen Yu
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two event datasets (N-Caltech101 and N-Cars) demonstrate that Event Drop can significantly improve the generalization performance across a variety of deep networks. |
| Researcher Affiliation | Academia | 1 College of Computer Science, Chongqing University, China 2 School of Computing, National University of Singapore, Singapore 3 Institute of Data Science, German Aerospace Center, Germany 4 Department of Precision Instrument, Tsinghua University, China |
| Pseudocode | Yes | Algorithm 1: Procedures of augmenting event data with Event Drop |
| Open Source Code | Yes | We have implemented Event Drop in Py Torch and the source code is available at https://github.com/fuqianggu/Event Drop. |
| Open Datasets | Yes | We evaluate the proposed Event Drop augmentation technique using two public event datasets: N-Caltech101 [Orchard et al., 2015] and N-Cars [Sironi et al., 2018]. |
| Dataset Splits | Yes | We perform early stopping on a validation set using the splits provided by the EST [Gehrig et al., 2019] on N-Caltech101 and 20% of the training data on N-Cars. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper mentions implementing Event Drop in PyTorch, but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The Adam optimizer is used to train the model by minimizing the cross-entropy loss. The initial learning rate is set to 1 10 4 until the iteration reaches up to 100, after which the learning rate is reduced by a factor of 0.5 every 10 iterations. The total number of iterations is set to 200. We use a batch size of 4 for both datasets. |