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 [1].
Representation Subspace Distance for Domain Adaptation Regression
Authors: Xinyang Chen, Sinan Wang, Jianmin Wang, Mingsheng Long
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is evaluated on three domain adaptation regression benchmarks, two of which are constructed in this paper. Our method outperforms the state-of-the-art methods significantly, forming early positive results in the deep regime. |
| Researcher Affiliation | Academia | 1School of Software, BNRist, Tsinghua University. Xinyang Chen <EMAIL>. Correspondence to: Mingsheng Long <EMAIL>. |
| Pseudocode | No | No section or figure explicitly labeled "Pseudocode" or "Algorithm". |
| Open Source Code | Yes | The code is available at github.com/thuml/Domain-Adaptation-Regression. |
| Open Datasets | Yes | d Sprites1 (Higgins et al., 2017) is a standard 2D synthetic dataset for deep representation learning. It is composed of three domains each with 737,280 images: Color (C), Noisy (N) and Scream (S). The example images are shown in Figure 4. In every image, there are ๏ฌve factors of variations, details illustrated in Table 1. https://github.com/deepmind/ dsprites-dataset. MPI3D2 (Gondal et al., 2019) is a simulation-to-real dataset of 3D objects. It has three domains: Toy (T), Realisti C (RC) and Rea L (RL). Each domain contains 1,036,800 images... https://github.com/rr-learning/ disentanglement_dataset. Biwi Kinect (Fanelli et al., 2013) is a real-world dataset for head pose estimation. |
| Dataset Splits | Yes | We employ IWCV (Sugiyama et al., 2007), a model selection method for domain adaptation, to determine the hyper-parameters and the number of iterations for all methods. |
| Hardware Specification | Yes | We use Py Torch3 with Titan V to implement our methods and ๏ฌne-tune Res Net-18 |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify a version number (e.g., PyTorch 1.x or 2.x). The footnote link provided is to the general PyTorch website. |
| Experiment Setup | Yes | The learning rates of layers trained from scratch are set to 10 times those of ๏ฌne-tuned layers. The batch size is b = 36. We use mini-batch SGD with a momentum of 0.95 with the learning rate of 0.1 and the progressive training strategies of DANN (Ganin et al., 2016). Labels are all normalized to [0, 1] to eliminate the effects of diverse scales in regression values, where the activation of the regressor is Sigmoid. |