title: Backward-Compatible Aligned Representations via an Orthogonal
    Transformation Layer
summary: 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.
paper_authors._id: 66c73f8e3fa97c9db6245365
paper_authors.name: Simone Ricci
paper_authors._id: 66c73f8e3fa97c9db6245366
paper_authors.name: Niccolò Biondi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-22T19:50:12.110Z
paper_authors._id: 669edb04cd29e1b976a64c73
paper_authors.avatarUrl: /avatars/fabc006d74d567110dfd0681b7ebf425.svg
paper_authors.fullname: Niccolò Biondi
paper_authors.type: user
paper_authors.user: NiccoBiondi
paper_authors._id: 66c73f8e3fa97c9db6245367
paper_authors.name: Federico Pernici
paper_authors._id: 66c73f8e3fa97c9db6245368
paper_authors.name: Alberto Del Bimbo
paper_title: Backward-Compatible Aligned Representations via an Orthogonal
    Transformation Layer
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Visual retrieval systems backfilling process backward-compatible training orthogonal transformation representation space OCA approach compatibility accuracy