Your Classifier can Secretly Suffice Multi-Source Domain Adaptation
Authors: Naveen Venkat, Jogendra Nath Kundu, Durgesh Singh, Ambareesh Revanur, Venkatesh Babu R
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive evaluation of our approach over five benchmark datasets, with two popular CNN backbone models (Res Net-50, Res Net-101 [16]) and derive insights from the empirical analysis. |
| Researcher Affiliation | Academia | Naveen Venkat Jogendra Nath Kundu Durgesh Kumar Singh Ambareesh Revanur R. Venkatesh Babu Video Analytics Lab, Indian Institute of Science, Bangalore |
| Pseudocode | Yes | Algorithm 1 SImp Al Self-supervised Implicit Alignment |
| Open Source Code | Yes | See code implementation1 for architecture, hyperparameter values, and instructions to reproduce the results. 1http://val.cds.iisc.ac.in/simpal |
| Open Datasets | Yes | Office-31 [47] dataset... Image CLEF2 dataset... Image Net (I) [46], Caltech-256 (C) [14], Pascal-VOC 2012 (P) [9]... Office-Caltech [12] dataset... Office-Home [59]... Domain Net [43] dataset... |
| Dataset Splits | Yes | For Image CLEF and Office-based datasets, we follow the evaluation protocol in MFSAN [68], while for Domain Net, we follow the protocol used in M3SDA [43]. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper states 'We implement our approach in Py Torch [41]' but does not specify the version number of PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use the Adam [19] optimizer, with learning rate 10 5 and weight decay 5 10 4 for stochastic optimization. The losses in Eq. 3 and Eq. 7 are alternatively optimized and the target agreement rate (Eq. 5) is periodically monitored for convergence. We set ne = 15 epochs as the update rate for the target pseudo-labels (line 12 in Algo. 1). |