Paper Citation Record · LEDGER

Aligned Contrastive Loss for Long-Tailed Recognition

As of 17 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2506.01071.

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

pith.paper-citation-record.v1
2506.01071 v1

Coverage vector

measured 51 of 51 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:17:36.749079Z

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

Source: pith, observed 2026-08-15T14:17:36.960645Z

Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation 496dfd59-4377-45c6-b18a-a7f73372880a · outbound

This paper cites A systematic study of the class imbalance problem in convo- lutional neural networks.Neural networks, 106:249–259,.

Aligned Contrastive Loss for Long-Tailed Recognition A systematic study of the class imbalance problem in convo- lutional neural networks.Neural networks, 106:249–259,

Reference 1

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Observation 54a77a6b-dd75-43e9-a478-dd5d3ece03ca · outbound

This paper cites What is the effect of im- portance weighting in deep learning? InInternational con- ference on machine learning, pages 872–881.

Aligned Contrastive Loss for Long-Tailed Recognition What is the effect of im- portance weighting in deep learning? InInternational con- ference on machine learning, pages 872–881

Reference 2

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Observation b02ee368-ed59-4a4b-aeba-9bda3e37eee6 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.Ad- vances in neural information processing systems, 33:9912– 9924, 2020.

Aligned Contrastive Loss for Long-Tailed Recognition Unsupervised learning of visual features by contrasting cluster assignments.Ad- vances in neural information processing systems, 33:9912– 9924, 2020

Reference 3

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Observation 08fc6982-e1f1-4a53-a886-15fb6bf32da7 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Aligned Contrastive Loss for Long-Tailed Recognition A simple framework for contrastive learning of visual representations

Reference 4

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Observation 7c44696b-44a9-4f56-9acf-b4a077e8a859 · outbound

This paper cites Big self-supervised mod- els are strong semi-supervised learners.Advances in neural information processing systems, 33:22243–22255, 2020.

Aligned Contrastive Loss for Long-Tailed Recognition Big self-supervised mod- els are strong semi-supervised learners.Advances in neural information processing systems, 33:22243–22255, 2020

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.275115Z

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Observation 55cc62b6-87b1-4c34-8b1b-9c10a97ae561 · outbound

This paper cites Exploring simple siamese rep- resentation learning.

Aligned Contrastive Loss for Long-Tailed Recognition Exploring simple siamese rep- resentation learning

Reference 6

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Observation ee7e4ee2-7633-433d-8599-416a02663995 · outbound

This paper cites An empirical study of training self-supervised vision transformers.

Aligned Contrastive Loss for Long-Tailed Recognition An empirical study of training self-supervised vision transformers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.251764Z

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Observation 7f290af6-da76-4a89-8774-8bf58c7cb9e6 · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

Aligned Contrastive Loss for Long-Tailed Recognition AutoAugment: Learning Augmentation Policies from Data

Reference 8

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Observation 05454b66-bd29-4bef-8377-bfa6ee782a55 · outbound

This paper cites Parametric contrastive learning.

Aligned Contrastive Loss for Long-Tailed Recognition Parametric contrastive learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.235025Z

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Observation f0390f71-3943-4576-92d1-1e0f589604a0 · outbound

This paper cites Generalized parametric contrastive learn- ing.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.

Aligned Contrastive Loss for Long-Tailed Recognition Generalized parametric contrastive learn- ing.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.219745Z

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Observation 4c97c0c1-836e-4b7a-b780-f6f705e0abbe · outbound

This paper cites Class-balanced loss based on effective number of samples.

Aligned Contrastive Loss for Long-Tailed Recognition Class-balanced loss based on effective number of samples

Reference 11

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This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

Aligned Contrastive Loss for Long-Tailed Recognition Improved Regularization of Convolutional Neural Networks with Cutout

Reference 12

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Aligned Contrastive Loss for Long-Tailed Recognition Unresolved cited work

Reference 13

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Observation b99af50c-e57c-4ab8-a3d2-d93bf5dbbbf2 · outbound

This paper cites Prob- abilistic contrastive learning for long-tailed visual recogni- tion.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

Aligned Contrastive Loss for Long-Tailed Recognition Prob- abilistic contrastive learning for long-tailed visual recogni- tion.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.181599Z

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Observation c132b04e-bbb7-43b3-9e69-72b720a99c22 · outbound

This paper cites Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error.

Aligned Contrastive Loss for Long-Tailed Recognition Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error

Reference 15

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Observation b6f428e0-128a-4d2f-857b-58c159a2e600 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020.

Aligned Contrastive Loss for Long-Tailed Recognition Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020

Reference 16

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Observation 3d2518be-621c-436e-8fa2-d70783ef8fda · outbound

This paper cites Deep residual learning for image recognition.

Aligned Contrastive Loss for Long-Tailed Recognition Deep residual learning for image recognition

Reference 17

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Observation f7d5b580-3b36-4891-8dc6-7fcf50f4c6a9 · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Aligned Contrastive Loss for Long-Tailed Recognition Momentum contrast for unsupervised visual rep- resentation learning

Reference 18

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Observation 470e16f4-cdb9-4d58-a4b6-be36ef8dbbad · outbound

This paper cites Disentangling label dis- tribution for long-tailed visual recognition.

Aligned Contrastive Loss for Long-Tailed Recognition Disentangling label dis- tribution for long-tailed visual recognition

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.137625Z

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Observation 0bb5a91a-dcd2-4629-b6e8-f681149d9da1 · outbound

This paper cites Subclass-balancing contrastive learning for long- tailed recognition.

Aligned Contrastive Loss for Long-Tailed Recognition Subclass-balancing contrastive learning for long- tailed recognition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.123506Z

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Observation b84a0ef7-704a-4806-a6c3-c3cac77dcfba · outbound

This paper cites Learning deep representation for imbalanced classifi- cation.

Aligned Contrastive Loss for Long-Tailed Recognition Learning deep representation for imbalanced classifi- cation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.108538Z

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Observation ecf8b53e-a8b5-4f7f-81d1-5dc9f1b75908 · outbound

This paper cites Decoupling Representation and Classifier for Long-Tailed Recognition.

Aligned Contrastive Loss for Long-Tailed Recognition Decoupling Representation and Classifier for Long-Tailed Recognition

Reference 22

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Observation 02f2950a-4e1e-488d-b305-ea77b4addee8 · outbound

This paper cites Exploring balanced feature spaces for representation learn- ing.

Aligned Contrastive Loss for Long-Tailed Recognition Exploring balanced feature spaces for representation learn- ing

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.093975Z

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Observation 7063bb18-13a9-41d7-ad0c-3d467e9ed22c · outbound

This paper cites Cost-sensitive learn- ing of deep feature representations from imbalanced data.

Aligned Contrastive Loss for Long-Tailed Recognition Cost-sensitive learn- ing of deep feature representations from imbalanced data

Reference 24

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Observation 32d5fa5e-72f6-4802-91c4-81da728bc424 · outbound

This paper cites Supervised contrastive learning.Advances in neural information processing systems, 33:18661–18673,.

Aligned Contrastive Loss for Long-Tailed Recognition Supervised contrastive learning.Advances in neural information processing systems, 33:18661–18673,

Reference 25

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This paper cites Targeted su- pervised contrastive learning for long-tailed recognition.

Aligned Contrastive Loss for Long-Tailed Recognition Targeted su- pervised contrastive learning for long-tailed recognition

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.055675Z

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Observation b9b04354-0472-4cbf-adf5-5102c7f013ac · outbound

This paper cites Inducing neural collapse in deep long- tailed learning.

Aligned Contrastive Loss for Long-Tailed Recognition Inducing neural collapse in deep long- tailed learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:33.041577Z

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Observation 0e6e469a-41cb-4666-a4d2-ee755622fb57 · outbound

This paper cites Large-scale long-tailed recognition in an open world.

Aligned Contrastive Loss for Long-Tailed Recognition Large-scale long-tailed recognition in an open world

Reference 28

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Observation 57d492da-2c6e-4a5b-913c-78c59fc89bcd · outbound

This paper cites Long-tail learning via logit adjustment.

Aligned Contrastive Loss for Long-Tailed Recognition Long-tail learning via logit adjustment

Reference 29

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This paper cites Decoupled Training for Long-Tailed Classification With Stochastic Representations.

Aligned Contrastive Loss for Long-Tailed Recognition Decoupled Training for Long-Tailed Classification With Stochastic Representations

Reference 30

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This paper cites Representation Learning with Contrastive Predictive Coding.

Aligned Contrastive Loss for Long-Tailed Recognition Representation Learning with Contrastive Predictive Coding

Reference 31

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This paper cites Dynamic sampling in convolutional neural networks for imbalanced data classification.

Aligned Contrastive Loss for Long-Tailed Recognition Dynamic sampling in convolutional neural networks for imbalanced data classification

Reference 32

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Observation 58e6488c-4956-44f6-9062-92c3da10e220 · outbound

This paper cites Balanced meta-softmax for long-tailed visual recog- nition.Advances in neural information processing systems, 33:4175–4186, 2020.

Aligned Contrastive Loss for Long-Tailed Recognition Balanced meta-softmax for long-tailed visual recog- nition.Advances in neural information processing systems, 33:4175–4186, 2020

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.997235Z

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This paper cites Relay back- propagation for effective learning of deep convolutional neu- ral networks.

Aligned Contrastive Loss for Long-Tailed Recognition Relay back- propagation for effective learning of deep convolutional neu- ral networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.984031Z

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Observation cf97e9aa-5b7f-48f9-8174-7d2e0251a81f · outbound

This paper cites Meta-weight-net: Learning an explicit mapping for sample weighting.Advances in neu- ral information processing systems, 32, 2019.

Aligned Contrastive Loss for Long-Tailed Recognition Meta-weight-net: Learning an explicit mapping for sample weighting.Advances in neu- ral information processing systems, 32, 2019

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.971232Z

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Observation 6d7dcac9-2be6-490e-8564-c9c14575b4dc · outbound

This paper cites Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels.

Aligned Contrastive Loss for Long-Tailed Recognition Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels

Reference 36

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Observation c53e64a8-0458-496c-8a8c-0a1a1372200b · outbound

This paper cites Long- tailed classification by keeping the good and removing the bad momentum causal effect.Advances in Neural Informa- tion Processing Systems, 33:1513–1524, 2020.

Aligned Contrastive Loss for Long-Tailed Recognition Long- tailed classification by keeping the good and removing the bad momentum causal effect.Advances in Neural Informa- tion Processing Systems, 33:1513–1524, 2020

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.958277Z

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Observation 0cd64b97-eb35-443b-9049-899afdb0c7cd · outbound

This paper cites The inaturalist species classification and de- tection dataset.

Aligned Contrastive Loss for Long-Tailed Recognition The inaturalist species classification and de- tection dataset

Reference 38

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Observation 807f2f41-5c02-4b40-a68a-79673cac1328 · outbound

This paper cites Contrastive learning based hybrid networks for long- tailed image classification.

Aligned Contrastive Loss for Long-Tailed Recognition Contrastive learning based hybrid networks for long- tailed image classification

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.936048Z

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Observation d1d07c33-8fde-418c-844f-b926137496f8 · outbound

This paper cites Learn- ing to model the tail.Advances in neural information pro- cessing systems, 30, 2017.

Aligned Contrastive Loss for Long-Tailed Recognition Learn- ing to model the tail.Advances in neural information pro- cessing systems, 30, 2017

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.922685Z

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Observation ada76156-f47a-4b2a-ade0-64c7bb88c70f · outbound

This paper cites Aggregated residual transformations for deep neural networks.

Aligned Contrastive Loss for Long-Tailed Recognition Aggregated residual transformations for deep neural networks

Reference 41

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This paper cites Decoupled contrastive learning for long-tailed recognition.

Aligned Contrastive Loss for Long-Tailed Recognition Decoupled contrastive learning for long-tailed recognition

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.899064Z

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Aligned Contrastive Loss for Long-Tailed Recognition Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:57:32.884674Z

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Observation cd4b1677-1134-44b7-8ccd-124a3759de5d · outbound

This paper cites Fairness-aware contrastive learning with partially annotated sensitive attributes.

Aligned Contrastive Loss for Long-Tailed Recognition Fairness-aware contrastive learning with partially annotated sensitive attributes

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.872301Z

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Observation 1946d27f-4bce-4f38-a835-ff9ab197ff9e · outbound

This paper cites Distribution alignment: A unified frame- work for long-tail visual recognition.

Aligned Contrastive Loss for Long-Tailed Recognition Distribution alignment: A unified frame- work for long-tail visual recognition

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.859314Z

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Observation 34a46ce7-e106-4797-8983-b6bdaec7cd8b · outbound

This paper cites Deep long-tailed learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10795–10816, 2023.

Aligned Contrastive Loss for Long-Tailed Recognition Deep long-tailed learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):10795–10816, 2023

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.845757Z

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Observation 09edfe46-7db3-43de-af05-ecc846b0d4a8 · outbound

This paper cites Im- proving calibration for long-tailed recognition.

Aligned Contrastive Loss for Long-Tailed Recognition Im- proving calibration for long-tailed recognition

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.831697Z

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Observation 9ec0e119-1b6d-49da-9ee0-0e13001059bc · outbound

This paper cites Places: A 10 million image database for scene recognition.IEEE transactions on pattern analysis and machine intelligence, 40(6):1452–1464, 2017.

Aligned Contrastive Loss for Long-Tailed Recognition Places: A 10 million image database for scene recognition.IEEE transactions on pattern analysis and machine intelligence, 40(6):1452–1464, 2017

Reference 48

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Observation 1bd47a6a-37eb-47bf-853d-4953e09f5909 · outbound

This paper cites Bbn: Bilateral-branch network with cumulative learn- ing for long-tailed visual recognition.

Aligned Contrastive Loss for Long-Tailed Recognition Bbn: Bilateral-branch network with cumulative learn- ing for long-tailed visual recognition

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.809154Z

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Observation 72cff29e-a661-4bf1-a323-c1881a6c0f5b · outbound

This paper cites Generalized logit adjustment: Calibrating fine-tuned models by removing label bias in foundation models.Advances in Neural Information Processing Systems, 36, 2024.

Aligned Contrastive Loss for Long-Tailed Recognition Generalized logit adjustment: Calibrating fine-tuned models by removing label bias in foundation models.Advances in Neural Information Processing Systems, 36, 2024

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.795710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 94ef28f9-20bd-4f97-b18e-1fc282f55db6 · outbound

This paper cites Balanced contrastive learn- ing for long-tailed visual recognition.

Aligned Contrastive Loss for Long-Tailed Recognition Balanced contrastive learn- ing for long-tailed visual recognition

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:57:32.782554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 80f84151-5d31-4cf2-ae4a-4623fbce90ec · inbound

CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification cites this paper.

CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification Aligned Contrastive Loss for Long-Tailed Recognition

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:17:36.966594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.