Adversarial Teacher-Student Representation Learning for Domain Generalization
Authors: Fu-En Yang, Yuan-Chia Cheng, Zu-Yun Shiau, Yu-Chiang Frank Wang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive image classification experiments on benchmark datasets in multiple and single source DG settings confirm that, our model exhibits sufficient generalization ability and performs favorably against state-of-the-art DG methods. |
| Researcher Affiliation | Collaboration | Fu-En Yang1,2 Yuan-Chia Cheng1 Zu-Yun Shiau1 Yu-Chiang Frank Wang1,2 1Graduate Institute of Communication Engineering, National Taiwan University, Taiwan 2ASUS Intelligent Cloud Services, Taiwan |
| Pseudocode | Yes | The pseudo code of our Adversarial Teacher-Student Representation Learning is summarized in Algorithm 1 in supplementary material. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described within the paper. |
| Open Datasets | Yes | We evaluate our method on several public benchmark datasets. PACS [35] is composed of four data domains... Office-Home [36] is comprised of four domains... Domain Net [37] is a recently proposed large-scale dataset... Due to page limitation, we additionally provide quantitative comparisons on VLCS [39] and Digit-DG [7] datasets in the supplementary material. |
| Dataset Splits | Yes | images from source domains are divided into the training split and the validation split, at a ratio of about 9:1. |
| Hardware Specification | Yes | In all our experiments, we implement our model using Py Torch and Dassl.pytorch [43] toolbox, and conduct training on a single NVIDIA TESLA V100 GPU with 32 GB memory. |
| Software Dependencies | No | The paper mentions using 'Py Torch and Dassl.pytorch [43] toolbox' but does not specify their version numbers, which are necessary for full reproducibility. |
| Experiment Setup | Yes | FS is trained with the SGD optimizer, with an initial learning rate of 0.0005, and a batch size of 32 for 60 epochs. The learning rate is decayed by 0.1 after 30 epochs. FT is updated via EMA with the momentum coefficient τ of 0.999 by default. |