On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Authors: Zahra Babaiee, Ramin Hasani, Mathias Lechner, Daniela Rus, Radu Grosu
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive empirical evidence showing that networks supplied with the OOCS edge representation gain accuracy and illumination-robustness compared to standard deep models. |
| Researcher Affiliation | Academia | 1CPS, TU Wien 2CSAIL, MIT 3IST Austria. |
| Pseudocode | Yes | Algorithm 1 Building and Training OOCS networks |
| Open Source Code | No | The paper does not provide a direct link to open-source code or explicitly state that the code for the described methodology is available. |
| Open Datasets | Yes | We used a subset of the Imagenet (Deng et al., 2009). We used the Norb dataset (Le Cun et al., 2004) to assess the robustness of our proposed OOCS-CNN architecture. We used the MNIST dataset (Le Cun & Cortes, 2010) to train our networks. |
| Dataset Splits | Yes | From these samples, we used 500 of each class for the training set, and 50 for each of the validation and test sets. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU models, CPU types) for running its experiments, only mentioning implementation in TensorFlow. |
| Software Dependencies | Yes | We implemented all models in Tensor Flow 2.3 (Abadi et al., 2016), and used Adam for optimization (Diederik & Ba, 2015) |
| Experiment Setup | Yes | We used Adam for optimization (Diederik & Ba, 2015) with a learning rate of 10^-4. All convolutional layers have 3x3 kernels and strides of 1. The max-pooling layers have 2x2 kernels and strides of 2. Dropouts (Srivastava et al., 2014) follow the fully connected layers, and the last layer predicts the category with a softmax function. |