In order to figure out generalization ability of different embedding method, I get feature vectors of CAR dataset which is training by different loss function.
For all loss function, the dataset is split into training data (first 100 categories) and validation data (rest). and it will show the t-sne for training, testing and all data following:
1. Lifted Structure (Batch All): training on Resnet-18.
2. Triplet loss (Semi-Hard Negative Mining): training on Resnet-18.
3.Easy Positive Semi-Hard Negative Mining: training on Resnet-18.
4. Npair Loss : training on Resnet-18:
5. Histogram loss: training on Resnet-50:
