Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'

Related tags

Deep Learning domain-adaptation neurips2021
Overview

Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation (NeurIPS 2021)

Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'. [project] [paper] (The codes are based on our G-SFDA (ICCV 2021))

Note: In the code, we do not explicitly compute the self-regularization loss (you will find the comment in the code), instead we do not explicitly remove the self features in the nearest neighbor retriving where the occurrence frequency of self feature acts as a dynamic weight.

Dataset preparing

Download the VisDA and Office-Home (use our provided image list files) dataset. And denote the path of data list in the code. The code is expected to reproduce the results with PyTorch 1.3 with cuda 10.0.

Checkpoint

You can find all the training log files and the weights (before and after the adaptation) on VisDA and Office-Home in this link. If you want to reproduce the results quickly, please use the provided source model.

VisDA

First train the model on source domain, then do target adaptation without source data:

python train_src.py

python train_tar.py

Office-Home

Code for Office-Home is in the 'office-home' folder.

sh train_src_oh.sh

sh train_tar_oh.sh

PointDA-10

Code in the folder 'pointDA-10' is based on PointDAN. Run the src.sh for source pretraining and tar.sh for source-free domain adaption.

Comments
  • Reproducing office31 results

    Reproducing office31 results

    Good morning, which hyperparameters should I use to reproduce your Office31 results? I tried with the same of Office-Home but I get results way lower than the ones in the paper.

    opened by andreamaracani 7
  • Where is code for self-regularization and L_div?

    Where is code for self-regularization and L_div?

    I have another question. In paper, you have loss for self-regularization and diversity. However, the code only use entropy of prediction as L_div. Why do you implement like this? Thanks.

    opened by chenslcool 5
  • paper and code implementation are different

    paper and code implementation are different

    Thank your for nice job! I notice that you use dot-product to measure simlarity between prediction in paper while use KL_div in code.

    paper: image

    code: https://github.com/Albert0147/NRC_SFDA/blob/1c2616039635710fa704d24a5cf7cfc9b47172bc/office-home/train_tar.py#L315-L317 I wonder why do you do this? Thanks!

    opened by chenslcool 2
  • Requesting requirements.txt or environment.yml

    Requesting requirements.txt or environment.yml

    Hi Shiqi,

    While trying to reproduce this work, I found some conflicting package versions. Can you please share the required package versions and dependencies used for implementing this work?

    Best, Anindya

    opened by mondalanindya 1
  • About reproduction on VISDA-C

    About reproduction on VISDA-C

    First of all, thanks for your excellent work. I have tried to reproduce the results of VISDA-C with your provided source model and codes, and I get a log as follows:

    Task: TV, Iter:866/12990; Accuracy on target = 82.84%
T: 95.86 82.47 83.18 65.42 94.33 96.53 87.63 80.9 89.38 87.2 88.81 42.34
Task: TV, Iter:1732/12990; Accuracy on target = 84.01%
T: 96.46 88.2 82.49 63.41 95.31 96.19 85.46 80.65 90.37 89.35 89.59 50.61
Task: TV, Iter:2598/12990; Accuracy on target = 84.38%
T: 96.65 88.6 83.54 62.27 95.25 96.34 87.01 79.97 89.76 91.32 88.62 53.24
Task: TV, Iter:3464/12990; Accuracy on target = 84.73%
T: 96.43 88.49 84.09 63.28 95.57 96.77 86.27 79.38 91.69 91.49 90.63 52.72
Task: TV, Iter:4330/12990; Accuracy on target = 84.87%
T: 96.63 89.09 83.45 57.55 95.61 96.53 86.18 80.65 92.66 91.54 91.64 56.87
Task: TV, Iter:5196/12990; Accuracy on target = 85.13%
T: 96.74 90.39 83.5 59.74 96.25 96.0 85.68 78.92 93.36 93.07 88.81 59.08
Task: TV, Iter:6062/12990; Accuracy on target = 84.71%
T: 97.04 90.59 83.71 57.33 96.03 96.82 81.88 79.82 92.28 92.33 90.49 58.22
Task: TV, Iter:6928/12990; Accuracy on target = 84.81%
T: 96.9 91.97 83.37 55.54 95.84 95.95 82.47 80.35 92.88 92.59 91.29 58.6
Task: TV, Iter:7794/12990; Accuracy on target = 84.18%
T: 96.98 91.42 84.18 48.25 95.65 96.53 82.3 79.72 93.56 93.12 90.53 57.91
Task: TV, Iter:8660/12990; Accuracy on target = 83.79%
T: 96.52 92.75 84.41 44.68 95.74 95.95 81.35 79.55 92.77 93.34 91.67 56.8
Task: TV, Iter:9526/12990; Accuracy on target = 83.87%
T: 97.2 90.5 84.52 41.13 96.35 96.29 86.37 79.88 93.54 91.93 92.33 56.43
Task: TV, Iter:10392/12990; Accuracy on target = 83.60%
T: 97.09 91.83 84.33 39.73 96.1 95.9 85.21 79.32 93.3 93.42 91.9 55.05
Task: TV, Iter:11258/12990; Accuracy on target = 83.73%
T: 97.04 90.96 86.76 40.48 96.5 95.76 87.01 79.18 93.6 93.12 90.93 53.48
Task: TV, Iter:12124/12990; Accuracy on target = 83.71%
T: 97.42 91.91 85.31 40.59 96.76 95.9 85.04 80.12 93.34 92.15 91.45 54.56
Task: TV, Iter:12990/12990; Accuracy on target = 83.59%
T: 96.98 91.31 84.54 39.24 96.4 95.76 85.84 80.05 93.8 93.56 92.23 53.42

    The training progress is totally different the one shown in the provided log. Can you kindly give me some suggestions for reproduction?

    opened by tiangarin 1
Owner
Shiqi Yang
PhD candidate @ LAMP group, Computer Vision Center, UAB.
Shiqi Yang
GitHub
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