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..
Meta Evidential Transformer for Few-Shot Open-Set Recognition
Authors: Hitesh Sapkota, Krishna Prasad Neupane, Qi Yu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world datasets demonstrate consistent improvement over existing competitive methods in unseen class recognition without deteriorating closed-set performance. |
| Researcher Affiliation | Collaboration | Hitesh Sapkota 1 2 Krishna Prasad Neupane 1 2 Qi Yu 2 1Amazon Inc. (Work was done at RIT, which is not relate to the position at Amazon) 2Rochester Institute of Technology (RIT). |
| Pseudocode | Yes | Algorithm 1 shows the overall training process of our proposed MET technique. As shown we use both open-set as well as closed-set loss to optimize the network parameters. Algorithm 2 shows the corresponding inference algorithm that leverages the cross-attention mechanism. |
| Open Source Code | Yes | F. Source Code For the source code, please click here. |
| Open Datasets | Yes | We conduct experimentation on multiple datasets, including Mini Image Net (Vinyals et al., 2016), Tired Image Net (Ren et al., 2018), Cifar100 (Krizhevsky et al., 2009), and Caltech101 (Fei-Fei et al., 2004). |
| Dataset Splits | Yes | Table 5 shows the dataset splits for four datasets: Mini Image Net, Tiered Image Net, Cifar100, and Caltech101. ... Table 5. Train/Evaluation/Test partition on different datasets. Split Mini Image Net ... Train Eval Test ... Closed-set 46 10 9 |
| Hardware Specification | No | The paper mentions using 'Res Net-12 as a backbone architecture' but does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper mentions using 'stochastic gradient descent (SGD)' and ResNet-12 but does not provide specific version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | For the training, stochastic gradient descent (SGD) is used with a total of 200 epochs. The initial learning rate of 0.002 is set and is decreased by 10% at an interval of every 20 epochs. The weight decay is set to 0.005 and λ is set to 1 throughout the experimentation. |