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..
Introspective Classification with Convolutional Nets
Authors: Long Jin, Justin Lazarow, Zhuowen Tu
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on benchmark datasets including MNIST, CIFAR10, and SVHN using state-of-the-art CNN architectures, and observe improved classification results. |
| Researcher Affiliation | Academia | Long Jin UC San Diego EMAIL Justin Lazarow UC San Diego EMAIL Zhuowen Tu UC San Diego EMAIL |
| Pseudocode | Yes | Algorithm 1 Outline of the reclassification-by-synthesis algorithm for discriminative classifier training. |
| Open Source Code | No | The paper does not provide concrete access to its own source code for the methodology described. |
| Open Datasets | Yes | We conduct experiments on three standard benchmark datasets, including MNIST, CIFAR-10 and SVHN. |
| Dataset Splits | Yes | We use the standard MNIST [24] dataset, which consists of 55, 000 training, 5, 000 validation and 10, 000 test samples. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of 'SGD optimizer' and 'Adam optimizer [17]' but does not provide specific version numbers for these or other software libraries/dependencies. |
| Experiment Setup | Yes | In our experiments, for the reclassification step, we use the SGD optimizer with mini-batch size of 64 (MNIST) or 128 (CIFAR-10 and SVHN) and momentum equal to 0.9; for the synthesis step, we use the Adam optimizer [17] with momentum term β1 equal to 0.5. |