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..
Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning
Authors: Bo Ye, Kai Gan, Tong Wei, Min-Ling Zhang
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets demonstrate that previous approaches may significantly hinder novel class learning, whereas our method strikingly balances the learning pace between seen and novel classes, achieving a remarkable 3% average accuracy increase on the Image Net dataset. |
| Researcher Affiliation | Academia | Bo Ye1,2, Kai Gan1,2, Tong Wei1,2 , Min-Ling Zhang1,2 1School of Computer Science and Engineering, Southeast University, Nanjing 211189, China 2Key Lab. of Computer Network and Information Integration (Southeast University), Mo E, China EMAIL |
| Pseudocode | No | The paper describes the proposed losses and the final objective function mathematically, but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/yebo0216best/LPS-main. |
| Open Datasets | Yes | We evaluate our method on three commonly used datasets, i.e., CIFAR-10, CIFAR-100 [Krizhevsky, 2009], and Image Net [Russakovsky et al., 2015]. |
| Dataset Splits | No | The paper specifies labeled ratios (10% or 50%) for seen classes but does not explicitly mention the use of a separate validation set or its split percentage/size for hyperparameter tuning or early stopping. |
| Hardware Specification | Yes | These experiments are conducted on a single NVIDIA 3090 GPU. |
| Software Dependencies | No | The paper mentions using Sim CLR and Rand Augment, and models like ResNet-18/50, but does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, CUDA, or specific library versions). |
| Experiment Setup | Yes | For CIFAR-10 and CIFAR-100, we utilize Res Net-18 as our backbone which is trained by the standard SGD with a momentum of 0.9 and a weight decay of 0.0005. We train the model for 200 epochs with a batch size of 512. For the Image Net dataset, we opt for Res Net-50 as our backbone. This choice also undergoes training via the standard SGD, featuring a momentum coefficient of 0.9 and a weight decay of 0.0001. The training process spans 90 epochs, with a batch size of 512. and The cosine annealing learning rate schedule is adopted on CIFAR and Image Net datasets. |