Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Do Different Deep Metric Learning Losses Lead to Similar Learned
  Features?   Recent studies have shown that many deep metric learning loss functions
perform very similarly under the same experimental conditions. One potential
reason for this unexpected result is that all losses let the network focus on
similar image regions or properties. In this paper, we investigate this by
conducting a two-step analysis to extract and compare the learned visual
features of the same model architecture trained with different loss functions:
First, we compare the learned features on the pixel level by correlating
saliency maps of the same input images. Second, we compare the clustering of
embeddings for several image properties, e.g. object color or illumination. To
provide independent control over these properties, photo-realistic 3D car
renders similar to images in the Cars196 dataset are generated. In our
analysis, we compare 14 pretrained models from a recent study and find that,
even though all models perform similarly, different loss functions can guide
the model to learn different features. We especially find differences between
classification and ranking based losses. Our analysis also shows that some
seemingly irrelevant properties can have significant influence on the resulting
embedding. We encourage researchers from the deep metric learning community to
use our methods to get insights into the features learned by their proposed
methods.

29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
2
The categories of this paper are: math