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README.md

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 A simple implementation of Deep Domain Confusion: Maximizing for Domain Invariance. 
 The project contains *Pytorch* code for fine-tuning *Alexnet* as well as *DDCnet*  implemented according to the original paper which adds an adaptation layer into the Alexnet. 
 The *office31* dataset used in the paper is also used in this implementation to test the performance of fine-tuning *Alexnet* and *DDCnet* with additional linear *MMD* loss. What’s more, 
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 # Run the work
 * Run command `python alextnet_finetune.py` to fine-tune a pretrained *Alexnet* on *office31* dataset with *full-training*.
 * Run command `python DDC.py` to fine-tune a pretrained *Alexnet* on *office31* dataset with *full-training*.
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 # Future work
 - [ ] Write data loader using  *down-sample* protocol mentioned in the paper instead of using *full-training* protocol.
 - [ ] Considering trying a tensorflow version to see if frameworks can have a difference on final experiment results.
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 # Reference
 Tzeng E, Hoffman J, Zhang N, et al. Deep domain confusion: Maximizing for domain invariance[J]. arXiv preprint arXiv:1412.3474, 2014.