Some code packages implementing my recent papers. Questions? Contact me judy _at_ gatech.edu.
| Package | Description | Dependencies |
|---|---|---|
| CyCADA | Cycle Consistent Adversarial Domain Adaptation. (Hoffman ICML`18) | PyTorch |
| LSDA | Large scale detection through adaptation. A model that adapts classification models into detectors. Release of a >7.5K category detector. (Hoffman NIPS`14) | Caffe |
| Caffe | Caffe is a sofeware package for training, finetuning, and evaluating CNNs for classification and detection. A faster and more flexible version of DeCAF. | - |
| CMA | Continuous manifold based unsupervised adaptation. Applied to two new datasets available on project page. (Hoffman CVPR`14) | - |
| DeCAF | DeCAF is a deep learning based feature (Donahue ICML`14). Check out the live demo here. | - |
| LS-MMDT | Faster implementation of MMDT which is integrated with a liblinear base package. This version should be used for large scale data sets. Makes use of the optimization algorithm presented at ICCV and NIPS recently (Rodner ICCV`13). | - |
| MMDT | Learns a category invariant transformation using max-margin constraints. Faster and better performance than da-transforms (Hoffman ICLR`13). | liblinear-weights, arc-t |
| Domain Discovery | Uses category constraints to automatically separate a heterogenous dataset into cohesive domains (Hoffman ECCV`12). | libsvm, arc-t |
| ARC-t | Learns a category invariant transform using similarity constraints. (Saenko ECCV`10, Kulis CVPR`11) | - |