Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification
Authors: Qi Han, Zhibo Tian, Chengwei Xia, Kun Zhan
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we show its superior performance. ... We evaluate Info Match on well-known benchmark datasets, including CIFAR-10/100 [Krizhevsky and Hinton, 2009], SVHN [Netzer et al., 2011], STL-10 [Coates et al., 2011], and Image Net [Deng et al., 2009]. |
| Researcher Affiliation | Academia | Qi Han, Zhibo Tian, Chengwei Xia, Kun Zhan School of Information Science and Engineering, Lanzhou University EMAIL |
| Pseudocode | Yes | Algorithm 1 The Info Match algorithm. |
| Open Source Code | Yes | The source code is available at https://github.com/kunzhan/Info Match. |
| Open Datasets | Yes | We evaluate Info Match on well-known benchmark datasets, including CIFAR-10/100 [Krizhevsky and Hinton, 2009], SVHN [Netzer et al., 2011], STL-10 [Coates et al., 2011], and Image Net [Deng et al., 2009]. |
| Dataset Splits | No | The paper mentions 'Info Match performance is then evaluated using the EMA with a parameter of 0.999' and 'self-adaptive thresholding method' but does not specify explicit validation dataset splits (e.g., percentages or counts for a dedicated validation set). |
| Hardware Specification | No | The paper mentions using specific model architectures like Res Net-50 and Wide Res Net variants, but it does not provide any specific details about the hardware (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Specifically, we employ standard stochastic gradient descent algorithm with cosine learning rate decay as the optimizer across all datasets, with an initial learning rate of 0.03 and a momentum of 0.9. For all experiments, we set the total number of iterations to 220. ... for Image Net, we maintain a batch size of 128 for both labeled and unlabeled samples, i.e., nb l = nb u = 128 ... For other datasets, we adjust the batch sizes to nb l = 64 and nb u = 448... Subsequently, we adjust the parameter λ that regulates the entropy bounds to 0.002. |