add: code for NBNN

DaomingLyu · Mar 15, 2018 · 0825309 · 0825309
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commit 0825309
Showing 1 changed file with 5 additions and 1 deletion.
diff --git a/code/README.md b/code/README.md
@@ -22,6 +22,8 @@
 	- [Matlab(Recommended!)](https://github.com/jindongwang/transferlearning/blob/master/code/MyTCA.m) | [Python](https://github.com/jindongwang/transferlearning/tree/master/code/TCA_python)
 - **GFK** (Geodesic Flow Kernel, CVPR-12) [2]
 	- [Matlab](https://github.com/jindongwang/transferlearning/blob/master/code/MyGFK.m)
+- **DA-NBNN** (Frustratingly Easy NBNN Domain Adaptation, ICCV-13) [39]
+	- [Matlab](https://github.com/enoonIT/nbnn-nbnl/tree/master/DANBNN_demo)
 - **JDA** (Joint Distribution Adaptation, ICCV-13) [3]
 	- [Matlab](https://github.com/jindongwang/transferlearning/blob/master/code/MyJDA.m)
 - **TJM** (Transfer Joint Matching, CVPR-14) [4]
@@ -185,4 +187,6 @@ accepted in International Conference on Computer Vision (ICCV 2015), Santiago, C
 
 [37] Shen J, Qu Y, Zhang W, et al. Adversarial representation learning for domain adaptation[J]. arXiv preprint arXiv:1707.01217, 2017.
 
-[38] Kim T, Cha M, Kim H, et al. Learning to discover cross-domain relations with generative adversarial networks[J]. arXiv preprint arXiv:1703.05192, 2017.
+[38] Kim T, Cha M, Kim H, et al. Learning to discover cross-domain relations with generative adversarial networks[J]. arXiv preprint arXiv:1703.05192, 2017.
+
+[39] Tommasi T, Caputo B. Frustratingly Easy NBNN Domain Adaptation[C]. international conference on computer vision, 2013: 897-904.