Transferable Curriculum for Weakly-Supervised Domain Adaptation
Authors: Yang Shu, Zhangjie Cao, Mingsheng Long, Jianmin Wang4951-4958
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments We evaluate TCL with state-of-the-art curriculum schemes and deep domain adaptation methods on three datasets. |
| Researcher Affiliation | Academia | Yang Shu, Zhangjie Cao, Mingsheng Long,B Jianmin Wang School of Software, Tsinghua University, China KLiss, MOE; BNRist; Research Center for Big Data, Tsinghua University, China {shuyang5656,caozhangjie14}@gmail.com {mingsheng,jimwang}@tsinghua.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Code and datasets will be available at github.com/thuml. |
| Open Datasets | Yes | Office-31 (Saenko et al. 2010) is a standard dataset for domain adaptation... Office-Home (Venkateswara et al. 2017) is a more challenging dataset for visual domain adaptation... Bing-Caltech (Bergamo and Torresani 2010) was created with Bing and Caltech-256 datasets. |
| Dataset Splits | No | The paper mentions selecting hyperparameters using 'cross validation' but does not provide specific dataset split percentages, sample counts, or a detailed splitting methodology for training, validation, and testing sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the implementation framework but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use Res Net-50 pre-trained on the Image Net dataset (Russakovsky et al. 2015) as our base model, and add a fullyconnected bottleneck layer before its classifier layer. Since the dataset is relatively small and the source domain is noisy, we fine-tune only the last residual block of the Res Net-50 model, and train the bottleneck layer, the classifier layer and the domain discriminator from scratch. Before using the curriculum, we pre-train our network on noisy data for a few epochs, which is better than random initialization. The tradeoff hyper-parameter λ is selected according to magnitudes of the two terms in Eq. (6), and the threshold γ is selected according to the distribution of loss values using cross validation. We use mini-batch SGD with momentum of 0.9 and the same learning rate strategy in (Ganin et al. 2016). |