Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
Authors: Mehdi Sajjadi, Mehran Javanmardi, Tolga Tasdizen
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed method on several benchmark datasets. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering University of Utah {mehdi, mehran, tolga}@sci.utah.edu |
| Pseudocode | No | The paper describes the proposed method and loss functions but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using existing frameworks (cuda-convnet [37] and sparse convolutional networks [38, 39]) but does not state that the code for the proposed methodology is openly provided. |
| Open Datasets | Yes | We show the effect of the proposed unsupervised loss functions using Conv Nets on MNIST [2], CIFAR10 and CIFAR100 [34], SVHN [35], NORB [36] and ILSVRC 2012 challenge [5]. |
| Dataset Splits | Yes | We randomly select 10 samples from each class (total of 100 labeled samples). We use all available training data as the unlabeled set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'cuda-convnet' and 'sparse convolutional networks' frameworks but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In Eq. 1, we set n to be 4 for experiments conducted using cuda-convnet and 5 for experiments performed using sparse convolutional networks. |