Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization
Authors: Kien Do, Truyen Tran, Svetha Venkatesh7236-7244
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show clear improvements in classification errors of various CR based methods when they are combined with VBI or MUR or both. |
| Researcher Affiliation | Academia | Applied Artificial Intelligence Institute (A2I2), Deakin University, Geelong, Australia {k.do, truyen.tran, svetha.venkatesh}@deakin.edu.au |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided. The methodology is described using mathematical equations and textual explanations. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate our approaches on three standard benchmark datasets: SVHN, CIFAR-10 and CIFAR-100. |
| Dataset Splits | No | The paper mentions 'labeled and unlabeled training datasets' (Dl, Du) and uses different numbers of labeled samples (e.g., 500, 1000, 250 on SVHN; 1000, 2000, 4000, 10000 on CIFAR-10/100). However, it does not explicitly provide specific train/validation/test split percentages or absolute counts for the datasets in the main text, nor does it refer to standard splits with citations that would provide this detail. |
| Hardware Specification | No | The paper does not explicitly provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments in the main text. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch, TensorFlow, or specific Python versions) used in the experiments. |
| Experiment Setup | Yes | The paper discusses specific experimental setup details such as the coefficient of DKL (qφ(w) p(w)) in VBI, the radius r in MUR, and learning rates (α) and number of steps (s) for iterative approximations of x. For example, 'Fig. 2c shows the error of MT+MUR on CIFAR-10 with 1000 labels as a function of r (r {4, 7, 10, 20, 40}).' and 'We try both projected gradient ascent (PGA) and vanilla gradient ascent (GA) updates with the learning rate α varying in {0.1, 1.0, 10.0} and the number of steps s varying in {2, 5, 8}.' |