Teaching Semi-Supervised Classifier via Generalized Distillation
Authors: Chen Gong, Xiaojun Chang, Meng Fang, Jian Yang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The superiority of our algorithm to the stateof-the-art methods has also been demonstrated by the experiments on different datasets with various sources of privileged knowledge. |
| Researcher Affiliation | Collaboration | 1 School of Computer Science and Engineering, Nanjing University of Science and Technology 2 Jiangsu Key Laboratory of Image and Video Understanding for Social Security 3 Language Technologies Institute, Carnegie Mellon University 4 Tencent AI Lab |
| Pseudocode | No | The paper describes the proposed method using prose and mathematical equations, but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access to source code (e.g., repository link or explicit code release statement) for the methodology described. |
| Open Datasets | Yes | Specifically, the ORL dataset [Cai et al., 2006] is employed...we use a recent Wikipedia dataset [Pereira et al., 2014]...Specifically, a very challenging dataset CIFAR100 [Krizhevsky and Hinton, 2009] is employed here |
| Dataset Splits | Yes | In this paper, every compared method is evaluated by the 5-fold cross validation on each dataset, and the average accuracy over the outputs of the five independent runs are reported to assess the performance of a certain algorithm. Therefore, the training set in ORL contains 320 examples, in which we randomly select 80 examples into labeled set L and the remaining 240 examples constitute the unlabeled set U. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions models like Alex Net and VGGNet-16 for feature extraction but does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In GDSSL, the parameter λ is set to 0.4 by searching the grid {0.2, 0.4, 0.6, 0.8}. The temperature parameter T is tuned to 0.01. Besides, the trade-off parameters in (6) are α = β = 0.1. For fair comparison, we build the same 10-NN graph for the graph-based methods including Lap RLS, LPDGL, Re LISH, and our GDSSL. The 10-NN graph with kernel width ξ = 10 is built for Lap RLS, Re LISH, LPDGL, and GDSSL. |