Guided Dropout
Authors: Rohit Keshari, Richa Singh, Mayank Vatsa4065-4072
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation on multiple datasets including MNIST, CIFAR10, CIFAR100, SVHN, and Tiny Image Net demonstrate the efficacy of the proposed guided dropout. |
| Researcher Affiliation | Academia | Rohit Keshari, Richa Singh, Mayank Vatsa IIIT-Delhi, India {rohitk, rsingh, mayank}@iiitd.ac.in |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions links for other related dropout methods but not for its own proposed method. |
| Open Datasets | Yes | Five benchmark databases including MNIST (Le Cun et al. 1998), CIFAR10 (Krizhevsky and Hinton 2009), CIFAR100 (Krizhevsky and Hinton 2009), SVHN (Netzer et al. 2011), and Tiny Image Net (Tiny Image Net 2018) have been used to evaluate the proposed method. |
| Dataset Splits | Yes | The experiments utilize 50k training samples and 10k as the test samples. CIFAR100 has a similar protocol with 100 classes. The protocol for CIFAR100 also has 50k and 10k training-testing split. The SVHN dataset contains 73, 257 training samples and 26, 032 testing samples. Tiny Image Net dataset... with 100k and 10k samples for training and validation sets, respectively. The experiments are performed on Tiny Image Net database with three-fold cross validation. |
| Hardware Specification | Yes | Experiments are performed on a workstation with two 1080Ti GPUs under Py Torch (Paszke et al. 2017) programming platform. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al. 2017) programming platform' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Number of epoch, learning rate, and batch size are kept as 200, [10 2, ..., 10 5], and 64, respectively for all the experiments. Learning rate is started from 10 2 and is reduced by a factor of 10 at every 50 epochs. For conventional dropout, the best performing results are obtained at 0.2 dropout probability. In the proposed guided dropout, 40 epochs have been used to train the strength parameter. Once the strength parameter is trained, dropout probabilities for guided dropout (DR) are set to 0.2, 0.15, and 0.1 for 60, 50, and 50 epochs, respectively. However, after strength learning, dropout ratio for guided dropout (top-k) are set to [0.2, 0.0, 0.15, 0.0, 0.1, 0.0] for [10, 40, 10, 40, 10, 50] epochs, respectively. |