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..
Frequency Shuffling and Enhancement for Open Set Recognition
Authors: Lijun Liu, Rui Wang, Yuan Wang, Lihua Jing, Chuan Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various benchmarks demonstrate that the proposed Fre SH consistently trumps the stateof-the-arts by a considerable margin. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, CAS, Beijing, China 2School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China 3Department of Electronic Engineering,Tsinghua University EMAIL, EMAIL |
| Pseudocode | No | The paper describes the proposed methods using detailed text and figures (e.g., Figure 3 for Fre SH overview, Figure 4 for DWT), but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | we evaluate the proposed method on MNIST (Le Cun et al. 2010), SVHN (Netzer et al. 2011), CIFAR10 (Krizhevsky, Hinton et al. 2009), CIFAR+10, CIFAR+50, Tiny-Image Net (Le and Yang 2015). |
| Dataset Splits | Yes | Following standard OSR protocols (Guo et al. 2021; Neal et al. 2018), we follow the commonly used splits (Neal et al. 2018). |
| Hardware Specification | Yes | All experiments are conducted with NVIDIA RTX 3090 GPU support. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and VGG32 as the backbone network, but it does not specify versions for any programming languages, libraries, or other software dependencies. |
| Experiment Setup | Yes | We use the Adam optimizer with a batch size of 128 for 600 epochs. The learning rate starts at 0.1 and decays by a factor of 0.1 every 120 epochs. |