Conditional Adversarial Domain Adaptation
Authors: Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, Michael I. Jordan
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our models exceed state-of-the-art results on five benchmark datasets. ... We evaluate the proposed conditional domain adversarial networks with many state-of-the-art transfer learning and deep learning methods. |
| Researcher Affiliation | Academia | School of Software, Tsinghua University, China KLiss, MOE; BNRist; Research Center for Big Data, Tsinghua University, China University of California, Berkeley, Berkeley, USA {mingsheng, jimwang}@tsinghua.edu.cn caozhangjie14@gmail.edu jordan@berkeley.edu |
| Pseudocode | No | The paper contains mathematical formulations and descriptions of the model, but it does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Codes will be available at http://github.com/thuml/CDAN. |
| Open Datasets | Yes | Office-31 [42] is the most widely used dataset for visual domain adaptation... Image CLEF-DA1 is a dataset organized by selecting the 12 common classes shared by three public datasets... We investigate three digits datasets: MNIST, USPS, and Street View House Numbers (SVHN). Vis DA-20172 is a challenging simulation-to-real dataset... 1http://imageclef.org/2014/adaptation 2http://ai.bu.edu/visda-2017/ |
| Dataset Splits | Yes | It contains over 280K images across 12 classes in the training, validation and test domains. ... We conduct importance-weighted cross-validation (IWCV) [48] to select hyper-parameters for all methods. |
| Hardware Specification | No | The paper states 'We implement Alex Net-based methods in Caffe and Res Net-based methods in Py Torch.' but does not provide any specific hardware details such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper mentions software like 'Caffe' and 'Py Torch' but does not specify their version numbers or the versions of any other software dependencies, making the description not fully reproducible. |
| Experiment Setup | Yes | We fine-tune from Image Net pre-trained models [41]... We train the new layers and classifier layer through back-propagation, where the classifier is trained from scratch with learning rate 10 times that of the lower layers. We adopt mini-batch SGD with momentum of 0.9 and the learning rate annealing strategy as [13]: the learning rate is adjusted by ηp = η0(1 + αp) β, where p is the training progress changing from 0 to 1, and η0 = 0.01, α = 10, β = 0.75 are optimized by the importance-weighted cross-validation [48]. We adopt a progressive training strategy for the discriminator, increasing λ from 0 to 1 by multiplying to 1 exp( δp)/(1+exp( δp)), δ = 10. As CDAN performs stably under different parameters, we fix λ = 1 for all experiments. |