Metric Compatible Training for Online Backfilling in Large-Scale Retrieval

• URL: http://arxiv.org/abs/2301.03767v2
• Date: Thu, 19 Dec 2024 16:45:52 GMT
• Title: Metric Compatible Training for Online Backfilling in Large-Scale Retrieval
• Authors: Seonguk Seo, Mustafa Gokhan Uzunbas, Bohyung Han, Sara Cao, Ser-Nam Lim,
• Abstract summary: Backfilling is the process of re-extracting all gallery embeddings from upgraded models in image retrieval systems. We propose an online backfilling algorithm, which enables us to achieve a progressive performance improvement during the backfilling process. We incorporate a reverse transformation module for more effective and efficient merging, which is further enhanced by adopting a metric-compatible contrastive learning approach.
• Score: 67.72644952719791
• License:
• Abstract: Backfilling is the process of re-extracting all gallery embeddings from upgraded models in image retrieval systems. It inevitably requires a prohibitively large amount of computational cost and even entails the downtime of the service. Although backward-compatible learning sidesteps this challenge by tackling query-side representations, this leads to suboptimal solutions in principle because gallery embeddings cannot benefit from model upgrades. We address this dilemma by introducing an online backfilling algorithm, which enables us to achieve a progressive performance improvement during the backfilling process while not sacrificing the final performance of new model after the completion of backfilling. To this end, we first propose a simple distance rank merge technique for online backfilling. Then, we incorporate a reverse transformation module for more effective and efficient merging, which is further enhanced by adopting a metric-compatible contrastive learning approach. These two components help to make the distances of old and new models compatible, resulting in desirable merge results during backfilling with no extra computational overhead. Extensive experiments show the effectiveness of our framework on four standard benchmarks in various settings.

Related papers

• ReMatching Dynamic Reconstruction Flow [55.272357926111454]
  We introduce the ReMatching framework, designed to improve generalization quality by incorporating deformation priors into dynamic reconstruction models. The framework is highly adaptable and can be applied to various dynamic representations. Our evaluations on popular benchmarks involving both synthetic and real-world dynamic scenes demonstrate a clear improvement in reconstruction accuracy of current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-11-01T16:09:33Z)
• EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models [70.60381055741391]
  Image restoration challenges related to illposed problems, resulting in deviations between single model predictions and ground-truths. Ensemble learning aims to address these deviations by combining the predictions of multiple base models. We employ an expectation (EM)-based algorithm to estimate ensemble weights for prediction candidates. Our algorithm is model-agnostic and training-free, allowing seamless integration and enhancement of various pre-trained image restoration models.
  arXiv  Detail & Related papers  (2024-10-30T12:16:35Z)
• Backward-Compatible Aligned Representations via an Orthogonal Transformation Layer [20.96380700548786]
  Visual retrieval systems face challenges when updating models with improved representations due to misalignment between the old and new representations. Prior research has explored backward-compatible training methods that enable direct comparisons between new and old representations without backfilling. In this paper, we address achieving a balance between backward compatibility and the performance of independently trained models.
  arXiv  Detail & Related papers  (2024-08-16T15:05:28Z)
• SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning [63.93193829913252]
  We propose an innovative METL strategy called SHERL for resource-limited scenarios. In the early route, intermediate outputs are consolidated via an anti-redundancy operation. In the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead.
  arXiv  Detail & Related papers  (2024-07-10T10:22:35Z)
• Stationary Representations: Optimally Approximating Compatibility and Implications for Improved Model Replacements [20.96380700548786]
  Learning compatible representations enables the interchangeable use of semantic features as models are updated over time. This is particularly relevant in search and retrieval systems where it is crucial to avoid reprocessing of the gallery images with the updated model. We show that the stationary representations learned by the $d$-Simplex fixed classifier optimally approximate compatibility representation according to the two inequality constraints of its formal definition.
  arXiv  Detail & Related papers  (2024-05-04T06:31:38Z)
• Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts [52.39959535724677]
  We introduce an alternative solution to improve the generalization of image restoration models. We propose AdaptIR, a Mixture-of-Experts (MoE) with multi-branch design to capture local, global, and channel representation bases. Our AdaptIR achieves stable performance on single-degradation tasks, and excels in hybrid-degradation tasks, with fine-tuning only 0.6% parameters for 8 hours.
  arXiv  Detail & Related papers  (2023-12-12T14:27:59Z)
• FastFill: Efficient Compatible Model Update [40.27741553705222]
  FastFill is a compatible model update process using feature alignment and policy based partial backfilling. We show that previous backfilling strategies suffer from decreased performance and demonstrate the importance of both the training objective and the ordering in online partial backfilling.
  arXiv  Detail & Related papers  (2023-03-08T18:03:51Z)
• Towards Universal Backward-Compatible Representation Learning [29.77801805854168]
  backward-compatible representation learning is introduced to support backfill-free model upgrades. We first introduce a new problem of universal backward-compatible representation learning, covering all possible data split in model upgrades. We propose a simple yet effective method, dubbed Universal Backward- Training (UniBCT) with a novel structural prototype refinement algorithm.
  arXiv  Detail & Related papers  (2022-03-03T09:23:51Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.