Confident Anchor-Induced Multi-Source Free Domain Adaptation
Authors: Jiahua Dong, Zhen Fang, Anjin Liu, Gan Sun, Tongliang Liu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several representative datasets illustrate the superiority of our proposed CAi DA model. |
| Researcher Affiliation | Academia | Jiahua Dong1, 2 , Zhen Fang3 , Anjin Liu3, Gan Sun1 , Tongliang Liu4 1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences. 2University of Chinese Academy of Sciences. 3De SI Lab, AAII, University of Technology Sydney. 4TML Lab, University of Sydney. |
| Pseudocode | Yes | Algorithm 1 The Searching Process of Semantic-Nearest Confident Anchor xjc. and Algorithm 2 The Optimization of Our Model. |
| Open Source Code | Yes | The code is available at https://github.com/Learning-group123/CAi DA. |
| Open Datasets | Yes | Datasets: Office-31 [22] consists of three representative domains with 31 shared object categories in the office environment, i.e., Amazon (A), Webcam (W) and DSLR (D). Office-Caltech [18] is an extension dataset of Office-31 [22] by adding an additional subset called Caltech-256 (C) on it and extracting 10 common object classes among them. Office-Home [46] is composed of four different domains including Product (Pr), Clipart (Cl), Art (Ar), and Realworld (Re). Each of these subsets consists of 65 shared object categories. Digits-Five [41] contains five digit recognition subsets including MNIST-M (MM), USPS (UP), MNIST (MT), SVHN (SV) and Synthetic Digits (SY). |
| Dataset Splits | No | The information is insufficient. While the paper uses well-known datasets, it does not explicitly state the specific training, validation, and test splits (e.g., percentages, counts, or references to predefined splits) used for its experiments. |
| Hardware Specification | No | The information is insufficient. The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The information is insufficient. The paper does not provide specific version numbers for ancillary software components, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | Parameter Investigation: This subsection investigates the effects of hyper-parameters κ1 in a range of {0.1, 0.3, 0.5, 0.7, 0.9} and κ2 in a range of {10 4, 10 3, 10 2, 10 1, 1} on Office-31 [22] and Office-Caltech [18] datasets, as shown in Figure 3 (a)(b). It validates that our proposed model achieves stable performance over a wide range of hyper-parameters selection. Moreover, the best performance of our proposed model on target domain is obtained when κ1 = 0.7 and κ2 = 10 2. |