Balanced Open Set Domain Adaptation via Centroid Alignment
Authors: Mengmeng Jing, Jingjing Li, Lei Zhu, Zhengming Ding, Ke Lu, Yang Yang8013-8020
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three OSDA benchmarks verify that our method can significantly outperform the compared methods and reduce the proportion of the unknown samples being misclassified into known classes. |
| Researcher Affiliation | Academia | 1 University of Electronic Science and Technology of China 2 Shandong Normal University, 3 Department of Computer Science, Tulane University |
| Pseudocode | Yes | Algorithm 1 Unknown Samples Recognition Using EVT |
| Open Source Code | No | The paper does not include an explicit statement or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Office-31 (Saenko et al. 2010) includes 31 classes from 3 domains: A, W and D. Following (Saito et al. 2018b), we select 10 classes as known and 11 classes as unknown. Vis DA-2017 (Peng et al. 2017) contains 2 domains: Synthetic and Real. Each domain includes 12 classes. Following (Saito et al. 2018b), we take the first 6 classes as known and the remaining as unknown. Image-CLEF1 includes 4 domains: I, C, P and B. Each domain contains 12 classes. We use the first 6 classes in alphabetical order as the known and the rest as the unknown. |
| Dataset Splits | No | The paper mentions using "importance-weighted cross-validation" for hyperparameter tuning, but it does not specify explicit dataset splits (e.g., percentages or sample counts) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'adam' optimizer but does not provide specific version numbers for software libraries, frameworks, or programming languages used for implementation. |
| Experiment Setup | Yes | We adopt adam (JLB 2015) to optimize these models with learning rate 4e-4 for S-VAE models and 1e-3 for the classifier. All the learning rate decreases during the training following an inverse decay scheduling. As for the hyperparameters, we get the optimal hyperparameters through importance-weighted cross-validation (Sugiyama, Krauledat, and M Aˇzller 2007). As our method performs stably under some hyperparameters, we fix the centroid update rate α = 0.2, the tail size η = 0.02, the threshold ζ = 0.98, and the margin angle m = 90 across all the experiments. In addition, for Office-31 and Image-CLEF, we set λ = 1.0, γ = 1.0. For Vis DA-2017, we set λ = 0.5, γ = 0.5. |