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..
Classification with Rejection: Scaling Generative Classifiers with Supervised Deep Infomax
Authors: Xin Wang, Siu Ming Yiu
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that SDIM with rejection policy can effectively reject illegal inputs, including adversarial examples and out-of-distribution samples. |
| Researcher Affiliation | Academia | Xin Wang and Siu Ming Yiu The University of Hong Kong EMAIL |
| Pseudocode | No | The paper describes the framework and methods in text and diagrams (Figure 1), but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to 'open sourced code' for a baseline model (ABS) in a footnote, but does not provide concrete access to the source code for the methodology (SDIM) described in this paper. |
| Open Datasets | Yes | We evaluate the effectiveness of the rejection policy of SDIM on four image datasets: MNIST, Fashion MNIST (both resized to 32 32 from 28 28); and CIFAR10, SVHN. |
| Dataset Splits | No | The paper mentions 'training set' and 'test sets' but does not explicitly provide details about training/validation/test dataset splits (percentages, counts, or specific split methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Throughout our experiments, we use α = β = γ = 1 in the loss function. We set d = 64 in all our experiments. For encoder of SDIM, we use Res Net [He et al., 2016] on 32 32 with a stack of 8n + 2 layers, and 4 filter sizes {32, 64, 128, 256}. The architecture is summarized as: output map size 32 32 16 16 8 8 4 4 # layers 1 + 2n 2n 2n 2n # filters 32 64 128 256 |