6.4 Loss Components

We validated the proposed technical improvements in Sec. 6.1 by adding them incrementally. This section
analyzes the performance hit observed if we ablate speciﬁc loss terms, starting from our best-performing
model. We ablate the importance of the KoLeo loss and the impact of the masked image modeling term.
For both, we report performance on ImageNet-1k using a linear classiﬁer, ADE-20k segmentation using a
linear classiﬁer, and nearest-neighbor image retrieval on Oxford-M. Table 3a shows the impact of using the
KoLeo loss. We see that the instance retrieval performance improves by more than 8%, conﬁrming that this
term helps spread features in the output space. At the same time, the other metrics do not suﬀer from this
regularization. In Table 3b, we show the impact of using the masked image modeling term from iBOT. This
term is critical for dense prediction tasks, leading to almost 3% performance improvement.

6.5 Impact of Knowledge Distillation
DINOv2- Learning Robust Visual Features without Supervision