CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation
Authors: Ankit Singh
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have empirically shown that both of these modules complement each other to achieve superior performance. Experiments on three well-known domain adaptation benchmark datasets, namely Domain Net, Office-Home, and Office31, demonstrate the effectiveness of our approach. ... We perform extensive ablation experiments highlighting the role of different components of our framework. |
| Researcher Affiliation | Academia | Ankit Singh Department of Computer Science Indian Institute of Technology, Madras singh.ankit@cse.iitm.ac.in |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The link provided (https://github.com/Vision Learning Group/SSDA_MME) is for the data-splits and settings of a prior work (MME [46]), not for the open-source code of the CLDA methodology described in this paper. |
| Open Datasets | Yes | We evaluate the effectiveness of our approach on three different domain adaptation datasets: Domain Net [43], Office-Home [56] and Office31 [45]. |
| Dataset Splits | Yes | For the fair comparison, we use the data-splits (train, validation, and test splits) released by [46] on Github 1. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments. It only mentions using Resnet34 and Alexnet as backbone networks and PyTorch. |
| Software Dependencies | No | All our experiments are performed using Pytorch [40]. (No version specified) |
| Experiment Setup | Yes | We use an identical set of hyperparameters (α = 4, β = 1 ) across all our experiments other than minibatch size. We have set τ = 5 in our experiments. Resnet34 experiments are performed with minibatch size, B = 32 and Alexnet models are trained with B = 24. We use µ = 4 for all our experiments. We use SGD optimizer with a momentum of 0.9 and an initial learning rate of 0.01 with cosine learning rate decay for all our experiments. Weight decay is set to 0.0005 for all our models. |