Classification and Representation Joint Learning via Deep Networks
Authors: Ya Li, Xinmei Tian, Xu Shen, Dacheng Tao
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on several benchmark image classification datasets, and the results demonstrate the effectiveness of our proposed method. |
| Researcher Affiliation | Collaboration | CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, University of Science and Technology of China, China UBTECH Sydney Artificial Intelligence Institute, SIT, FEIT, The University of Sydney, Australia |
| Pseudocode | Yes | Algorithm 1 Parameter updating algorithm of our proposed co-learning network |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | To evaluate the effectiveness of our proposed method, we conduct various experiments on three benchmark datasets: MNIST, SVHN, and CIFAR10. |
| Dataset Splits | No | The paper specifies training and test sets (e.g., "60000 28 28 handwritten digits of 10 classes and a test set of 10000 samples" for MNIST) and mentions different amounts of training samples used, but does not explicitly state the use of a separate validation split for reproduction. |
| Hardware Specification | No | The paper mentions "GPU" in a general context regarding storage limitations, but does not provide specific hardware details such as GPU/CPU models, processors, or memory specifications used for experiments. |
| Software Dependencies | No | All experiments are implemented using the CAFFE deep learning framework [Jia et al., 2014]. No specific version number is provided for CAFFE or any other software. |
| Experiment Setup | Yes | We use Le Net to conduct all experiments on MNIST. Le Net consists of 2 convolutional layers, and both of these layers are followed by a 2 2 max-pooling layers. Then, two fully connected layers are followed. The only preprocessing of the data is a global normalization that normalizes the pixel values of the image to 0-1. The parameters b and m are introduced for the pairwise loss, and λ is a trade-off parameter. The learning algorithm is presented in Algorithm 1. We preprocess the images using local contrast normalization. We adopt a network similar to that used in [Hoffer and Ailon, 2015], which consists of 4 convolutional layers and 1 fully connected layer. The images are preprocessed by performing global contrast normalization... Then, ZCA whitening is performed. |