Backward-Compatible Aligned Representations via an Orthogonal
  Transformation Layer
Visual retrieval systems face significant challenges when updating models
with improved representations due to misalignment between the old and new
representations. The costly and resource-intensive backfilling process involves
recalculating feature vectors for images in the gallery set whenever a new
model is introduced. To address this, prior research has explored
backward-compatible training methods that enable direct comparisons between new
and old representations without backfilling. Despite these advancements,
achieving a balance between backward compatibility and the performance of
independently trained models remains an open problem. In this paper, we address
it by expanding the representation space with additional dimensions and
learning an orthogonal transformation to achieve compatibility with old models
and, at the same time, integrate new information. This transformation preserves
the original feature space's geometry, ensuring that our model aligns with
previous versions while also learning new data. Our Orthogonal Compatible
Aligned (OCA) approach eliminates the need for re-indexing during model updates
and ensures that features can be compared directly across different model
updates without additional mapping functions. Experimental results on CIFAR-100
and ImageNet-1k demonstrate that our method not only maintains compatibility
with previous models but also achieves state-of-the-art accuracy, outperforming
several existing methods.
66c73f8e3fa97c9db6245365
Simone Ricci
66c73f8e3fa97c9db6245366
Niccolò Biondi
claimed_verified
2024-08-22T19:50:12.110Z
669edb04cd29e1b976a64c73
/avatars/fabc006d74d567110dfd0681b7ebf425.svg
Niccolò Biondi
user
NiccoBiondi
66c73f8e3fa97c9db6245367
Federico Pernici
66c73f8e3fa97c9db6245368
Alberto Del Bimbo
Backward-Compatible Aligned Representations via an Orthogonal
  Transformation Layer
Visual retrieval systems face significant challenges when updating models
with improved representations due to misalignment between the old and new
representations. The costly and resource-intensive backfilling process involves
recalculating feature vectors for images in the gallery set whenever a new
model is introduced. To address this, prior research has explored
backward-compatible training methods that enable direct comparisons between new
and old representations without backfilling. Despite these advancements,
achieving a balance between backward compatibility and the performance of
independently trained models remains an open problem. In this paper, we address
it by expanding the representation space with additional dimensions and
learning an orthogonal transformation to achieve compatibility with old models
and, at the same time, integrate new information. This transformation preserves
the original feature space's geometry, ensuring that our model aligns with
previous versions while also learning new data. Our Orthogonal Compatible
Aligned (OCA) approach eliminates the need for re-indexing during model updates
and ensures that features can be compared directly across different model
updates without additional mapping functions. Experimental results on CIFAR-100
and ImageNet-1k demonstrate that our method not only maintains compatibility
with previous models but also achieves state-of-the-art accuracy, outperforming
several existing methods.
The Orthogonal Compatible Aligned (OCA) approach expands the representation space and uses an orthogonal transformation to maintain backward compatibility while integrating new information, achieving state-of-the-art accuracy in visual retrieval systems without re-indexing.
Visual retrieval systems backfilling process backward-compatible training orthogonal transformation representation space OCA approach compatibility accuracy