EventDrop: Data Augmentation for Event-based Learning
Authors: Fuqiang Gu, Weicong Sng, Xuke Hu, Fangwen Yu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | 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. |