Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation
Authors: Naveen Venkat, Jogendra Nath Kundu, Durgesh Singh, Ambareesh Revanur, Venkatesh Babu R
NeurIPS 2020 | Venue PDF | 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). |