Improving CNN Performance with Min-Max Objective
Authors: Weiwei Shi, Yihong Gong, Jinjun Wang
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with shallow and deep models on four benchmark datasets including CIFAR10, CIFAR-100, SVHN and MNIST demonstrate that CNN models trained with the Min-Max objective achieve remarkable performance improvements compared to the corresponding baseline models. |
| Researcher Affiliation | Academia | Weiwei Shi, Yihong Gong , Jinjun Wang Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University, Xi an 710049, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | All the models are implemented using the Caffe platform [Jia et al., 2014] from scratch without pre-training. The paper does not provide concrete access to its own source code. |
| Open Datasets | Yes | We conduct performance evaluations using four benchmark datasets, i.e. CIFAR-10, CIFAR-100, MNIST and SVHN. |
| Dataset Splits | Yes | The Street View House Numbers (SVHN) dataset [Netzer et al., 2011] consists of 630,420 color images of 32x32 pixels in size, which are divided into the training set, testing set and an extra set with 73,257, 26,032 and 531,131 images, respectively. [...] 400 samples per class selected from the training set and 200 samples per class from the extra set were used for validation, while the remaining 598,388 images of the training and the extra sets were used for training. The validation set was only used for tuning hyper-parameters and was not used for training the model. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | All the models are implemented using the Caffe platform [Jia et al., 2014] from scratch without pre-training. No specific version numbers for software dependencies are provided. |
| Experiment Setup | Yes | For simplicity, we set k1 = 5, k2 = 10 for all the experiments, and it is possible that better results can be obtained by tuning k1 and k2. σ2 is empirically selected from {0.1, 0.5}, and λ ∈ [10^-6, 10^-9]. |