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..
Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification
Authors: Jian Yang, Kai Zhu, Kecheng Zheng, Yang Cao
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on widely used benchmarks (i.e., IIRC-CIFAR, IIRC-Image Net-lite, IIRC-Image Net-Subset, and IIRC-Image Net-full) demonstrate the superiority of our proposed method over the state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | Jian Yang1 Kai Zhu1, , Kecheng Zheng2 Yang Cao1,3, 1 University of Science and Technology of China 2 Ant Group 3 Institute of Artificial Intelligence, Hefei Comprehensive National Science Center |
| Pseudocode | Yes | Algorithm 1 Acquisition of Hierarchical Semantic Relationship |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Supplementary Materials. |
| Open Datasets | Yes | According to the semantic relevance among labels, CIFAR100 [13] and Image Net [6] datasets are rearranged to form the two-level hierarchy datasets [1]. |
| Dataset Splits | Yes | The validation set also follows the incomplete information setting. |
| Hardware Specification | No | The paper states, 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Supplementary Materials.' However, the main paper itself does not explicitly describe the hardware used for experiments. |
| Software Dependencies | No | The paper mentions methods and components like 'RBF kernel' and 'BCEWith Logits Loss', but it does not specify software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | Specifically, we use the RBF distance [19] to calculate the representation extension loss. ... γ denotes hyper-parameters for balancing the losses. Moreover, in our experiments, γ is set as 10.0. A detailed description of the hyperparameter selection is shown in supplementary material B.2. ... IIRC-CIFAR. Ten superclasses are set up, each with about 4 to 8 subclasses. In incremental phases, each new phase introduces five classes. IIRC-CIFAR involves 22 phases with ten preset class orders called phase configuration for multiple tests. |