We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| agrija9/deep-unsupervised-domain-adaptation | reimplementation | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| plot_loss_acc | Not yet run | agrija9/deep-unsupervised-domain-adaptation/DDC/plot_loss_acc.py pointer only (licence: NONE) · get_code("983a38597af964e0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning deep models in a new domain can require a significant amount of data, which for many applications is simply not available. We propose a new CNN architecture which introduces an adaptation layer and an additional domain confusion loss, to learn a representation that is both semantically meaningful and domain invariant. We additionally show that a domain confusion metric can be used for model selection to determine the dimension of an adaptation layer and the best position for the layer in the CNN architecture. Our proposed adaptation method offers empirical performance which exceeds previously published results on a standard benchmark visual domain adaptation task.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1412.3474")
get_code_for_paper("1412.3474")
have("1412.3474")
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