Paper Citation Record · LEDGER

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features

As of 15 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2412.01555.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.01555 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:21:06.339224Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T11:16:04.151281Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

verified exact6
verified fuzzy7
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3eeb0445-dd26-4da8-90d7-bffb14051b10 · outbound

This paper cites AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications,

Reference 1

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Observation 7d16300a-5368-4df0-9b1d-71583ca4f556 · outbound

This paper cites E- Commerce Recommendation Applications,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features E- Commerce Recommendation Applications,

Reference 2

Resolution: verified exact; doi, observed 2026-08-12T04:21:06.567274Z

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Observation 18ca531a-72ca-4311-9210-de9def255ceb · outbound

This paper cites Content-based image retrieval for medical diagnosis using fuzzy clustering and deep learning,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Content-based image retrieval for medical diagnosis using fuzzy clustering and deep learning,

Reference 3

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Observation 2599a0a5-96a2-4e54-a7d6-2cf8a9d38fd9 · outbound

This paper cites State-of-the-art in artificial neural network applications: A survey,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features State-of-the-art in artificial neural network applications: A survey,

Reference 4

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This paper cites Artificial neural networks: fundamentals, computing, design, and application,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Artificial neural networks: fundamentals, computing, design, and application,

Reference 5

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Observation b0ec20d9-79ba-497a-84b8-0091a57226f7 · outbound

This paper cites A comprehensive review for industrial applicability of artificial neural networks,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features A comprehensive review for industrial applicability of artificial neural networks,

Reference 6

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Observation 6248712a-0012-41d1-b5a2-7f63615eb7a1 · outbound

This paper cites The Faiss library.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features The Faiss library

Reference 7

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Observation f8969aa9-949d-4a29-adf1-1a60edc15526 · outbound

This paper cites GitHub - spotify/annoy: Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features GitHub - spotify/annoy: Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

Reference 8

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Observation c35b7996-e731-4453-952c-5946fc3fcc11 · outbound

This paper cites Comparison of different ANN techniques in river flow prediction,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Comparison of different ANN techniques in river flow prediction,

Reference 9

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Observation dc537241-8166-45e0-bc84-b4f75d3ce204 · outbound

This paper cites Domain-specific language models pre-trained on construction management systems corpora,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Domain-specific language models pre-trained on construction management systems corpora,

Reference 10

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Observation 46c97d5f-9e06-4b2b-8a8d-2ac5c9a621e0 · outbound

This paper cites Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination,

Reference 11

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Observation 3d215bf0-4471-42ad-b8d1-56fdecd43cde · outbound

This paper cites Product quantization for nearest neighbor search,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Product quantization for nearest neighbor search,

Reference 13

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Observation 4a0b364f-9048-480c-9ae3-2b5459df42fa · outbound

This paper cites Billion-Scale Similarity Search with GPUs,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Billion-Scale Similarity Search with GPUs,

Reference 14

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This paper cites Curator: Efficient Indexing for Multi-Tenant Vector Databases,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Curator: Efficient Indexing for Multi-Tenant Vector Databases,

Reference 15

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Observation 06c932ac-33f8-47f1-8be4-30309f37cb88 · outbound

This paper cites Constrained Approximate Similarity Search on Proximity Graph.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Constrained Approximate Similarity Search on Proximity Graph

Reference 16

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Observation d1337513-e76e-497e-bc57-bd41e15b89f4 · outbound

This paper cites Curator: Efficient Indexing for Multi-Tenant Vector Databases.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Curator: Efficient Indexing for Multi-Tenant Vector Databases

Reference 17

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Observation d30811e6-11f6-4785-aeec-85bf6a260545 · outbound

This paper cites ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms

Reference 18

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Observation a2dc9727-fdc0-4a79-bbc6-f3686af7c1bc · outbound

This paper cites ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms,

Reference 20

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features A meta-learning configuration framework for graph-based similarity search indexes,

Reference 21

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features An efficient faiss-based search method for mass spectral library searching,

Reference 22

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Observation 6083f1ea-eae7-41c8-9b23-afeea884a1d3 · outbound

This paper cites Approximate Similarity Search with FAISS Framework Using FPGAs on the Cloud,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Approximate Similarity Search with FAISS Framework Using FPGAs on the Cloud,

Reference 23

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This paper cites An Investigation of Practical Approximate Nearest Neighbor Algorithms,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features An Investigation of Practical Approximate Nearest Neighbor Algorithms,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:21:07.670406Z

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Observation ba742e9c-3423-40d6-97f3-e9a1ba9f2d7e · outbound

This paper cites Efficient Medical Image Retrieval Using DenseNet and FAISS for BIRADS Classification.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Efficient Medical Image Retrieval Using DenseNet and FAISS for BIRADS Classification

Reference 25

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This paper cites Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing,

Reference 26

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Observation 27b8c769-9b1d-4917-9796-ebf2190bb040 · outbound

This paper cites Survey of vector database management systems,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Survey of vector database management systems,

Reference 27

Resolution: verified exact; doi, observed 2026-08-12T04:21:06.445005Z

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Observation 914e08ae-cb1f-4faf-915f-e0a68a64311c · outbound

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Fashion Product Images Dataset

Reference 28

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features The role of local dimensionality measures in benchmarking nearest neighbor search,

Reference 29

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Advanced data mining techniques,

Reference 30

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Adafactor: Adaptive Learning Rates with Sublinear Memory Cost,

Reference 31

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features A Review of the F-Measure: Its History, Properties, Criticism, and Alternatives,

Reference 32

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

Reference 33

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features High-dimensional signature compression for large-scale image classification,

Reference 34

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This paper cites The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,

Reference 35

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Optimization of Indexing Based on k-Nearest Neighbor Graph for Proximity Search in High-dimensional Data

Reference 36

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Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Announcing ScaNN: Efficient Vector Similarity Search

Reference 37

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This paper cites Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs,.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs,

Reference 39

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This paper cites Available: https://github.com/spotify/annoy.

Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features Available: https://github.com/spotify/annoy

Reference 2024

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Pith citing papers

Observation bfa5d040-4d9e-46de-9e5f-7bc91a526434 · inbound

GPU-Accelerated ANNS: Quantized for Speed, Built for Change cites this paper.

GPU-Accelerated ANNS: Quantized for Speed, Built for Change Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features

Reference 27

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