Co-regularized Alignment for Unsupervised Domain Adaptation
Authors: Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, Bill Freeman, Gregory Wornell
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed Co-regularized Domain Alignment (Co-DA) by instantiating it in the context of a recently proposed method VADA [35] which has shown state-of-the-art results on several benchmarks, and observe that Co-DA yields further significant improvement over it, establishing new state-of-the-art in several cases. For a fair comparison, we evaluate on the same datasets as used in [35] (i.e., MNIST, SVHN, MNIST-M, Synthetic Digits, CIFAR-10 and STL), and base our implementation on the code released by the authors3 to rule out incidental differences due to implementation specific details. |
| Researcher Affiliation | Collaboration | Abhishek Kumar MIT-IBM Watson AI Lab, IBM Research abhishk@us.ibm.com Prasanna Sattigeri MIT-IBM Watson AI Lab, IBM Research psattig@us.ibm.com Kahini Wadhawan MIT-IBM Watson AI Lab, IBM Research kahini.wadhawan@ibm.com Leonid Karlinsky MIT-IBM Watson AI Lab, IBM Research leonidka@il.ibm.com Rogerio Feris MIT-IBM Watson AI Lab, IBM Research rsferis@us.ibm.com William T. Freeman MIT billf@mit.edu Gregory Wornell MIT gww@mit.edu |
| Pseudocode | No | The paper describes the algorithmic instantiation of its approach in prose, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'base our implementation on the code released by the authors3' (referring to [35], VADA's authors), but does not explicitly state that the authors of *this* paper have released their own implementation code for Co-DA. |
| Open Datasets | Yes | For a fair comparison, we evaluate on the same datasets as used in [35] (i.e., MNIST, SVHN, MNIST-M, Synthetic Digits, CIFAR-10 and STL) |
| Dataset Splits | Yes | The hyperparameters are tuned by randomly selecting 1000 target labeled examples from the training set and using that for validation, following [35, 32]. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud computing instances used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam Optimizer' and 'batch-norm' and 'dropout' layers as part of the model and training process, but does not list any specific software dependencies or libraries with their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For VADA hyperparameters λce and λsv (Eq. 7), we fix their values to what were reported by Shu et al. [35]... For the domain alignment hyperparameter λd, we do our own search over the grid {10 3, 10 2}... The hyperparameter for target prediction agreement, λp, was obtained by a search over {10 3, 10 2, 10 1}. For hyperparameters in the diversity term, we fix λdiv = 10 2 and do a grid search for ν (Eq. 5) over {1, 5, 10, 100}. The hyperparameters are tuned by randomly selecting 1000 target labeled examples from the training set and using that for validation, following [35, 32]. We completely follow [35] for training our model, using Adam Optimizer (lr = 0.001, β1 = 0.5, β2 = 0.999) with Polyak averaging (i.e., an exponential moving average with momentum= 0.998 on the parameter trajectory), and train the models in all experiments for 80k iterations as in [35]. |