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Source: paper_references, paper_reference_links, observed 2026-08-03T04:08:22.605320Z
Paper Citation Record · LEDGER
As of 23 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2602.05845.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: paper_references, paper_reference_links, observed 2026-08-03T04:08:22.605320Z
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Pith citing papers itemized under the disclosed page cap.
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73 of 73 outbound references displayed
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Observation af611839-4ef4-47e9-a6c1-21855da01451 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Masked siamese net- works for label-efficient learning
Reference 1
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Observation ed67a835-be87-4932-b380-443c56664ab1 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Self-supervised learning from images with a joint-embedding predictive architecture
Reference 2
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Observation cd0f9334-989b-47ef-86ef-13b5ca8fa5aa · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Seeing the Whole in the Parts in Self-Supervised Representation Learning
Reference 3
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Observation 47e42dd2-d1b7-43ee-bb52-b58aeea8895b · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Multimae: Multi-modal multi-task masked autoen- coders
Reference 4
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Observation a93d9d79-bcdb-4302-a9e9-615c68ca979a · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Beit: Bert pre-training of image transformers
Reference 5
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Observation caea8d1c-2bfc-4f35-b587-793bdc4f6dff · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Vi- creg: Variance-invariance-covariance regularization for self- supervised learning
Reference 6
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Observation 0c672f3e-073e-4e89-b1f9-3f82129e8067 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Deep clustering for unsupervised learning of visual features
Reference 7
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Observation 4865159e-a34b-41bd-9157-b9d3cce6e433 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Unsupervised learn- ing of visual features by contrasting cluster assignments
Reference 8
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Observation 8c54a016-c87b-4266-819b-7735cd0425da · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Emerg- ing properties in self-supervised vision transformers
Reference 9
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Observation 3ec428f9-c199-4e08-84fa-cbec3f7144ae · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective A simple framework for contrastive learn- ing of visual representations
Reference 10
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Observation bd4931d8-ebc4-413d-ab5d-99076646229d · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Exploring simple siamese rep- resentation learning
Reference 11
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Observation 10e878a0-1195-40a3-86fe-d049edbc3598 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Improved Baselines with Momentum Contrastive Learning
Reference 12
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Observation b3b2edb3-6596-49f2-a91a-ceaa0d84cf80 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective An empiri- cal study of training self-supervised vision transformers
Reference 13
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Observation cecee87c-3cbf-4317-a0e1-a0222fd136b9 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Cluster and predict la- tent patches for improved masked image modeling.CoRR,
Reference 14
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Observation cba0488d-25e4-4c44-b01c-09d1573b69fb · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Adversarial Dependence Minimization
Reference 15
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Observation cc398f9d-f1fa-492c-89b7-8f131a135705 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Imagenet: A large-scale hierarchical image database
Reference 16
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Observation 36ff11e0-ffff-4842-a5e4-0ba4e3c7acd9 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Improved Regularization of Convolutional Neural Networks with Cutout
Reference 17
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Observation 7bbc278e-1ccb-404f-ae40-38c7052b306f · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Multi-task self- supervised visual learning
Reference 18
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Observation bea88949-8f93-402c-938c-6c81ba93c033 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Unsuper- vised visual representation learning by context prediction
Reference 19
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Observation a0a49ddb-cbde-426a-ada1-640cec093de7 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective An image is worth 16x16 words: Transformers for image recognition at scale
Reference 20
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Observation 2013b4c9-7deb-4025-b648-79edcb2ab4ef · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Whitening for self-supervised representation learning
Reference 21
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Observation fdc65874-e27c-4510-9d17-83f8c207b01d · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Multimodal Masked Autoencoders Learn Transferable Representations
Reference 22
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Observation 95cef672-25c6-47d9-9733-c400aef03edf · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Un- supervised representation learning by predicting image rota- tions
Reference 23
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Observation c760926a-001e-4d25-b655-ab51f66be988 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Reference 24
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Observation 9a33d36d-a78c-48ab-bf6c-2161a25fd69d · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Bootstrap your own latent-a new ap- proach to self-supervised learning.NeurIPS, 33:21271– 21284, 2020
Reference 25
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Observation 9d75952e-0f84-48ca-acfa-03f36f641382 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Unsupervised multi-task feature learning on point clouds
Reference 26
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Observation b08ee0c8-eebe-4e63-9562-7b7dfa6bb402 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Deep residual learning for image recognition
Reference 27
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Observation 58995d83-4784-4f0b-a0a8-e6553ceb3849 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Momentum contrast for unsupervised visual rep- resentation learning
Reference 28
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Observation b9b8c36f-0e4f-47d3-ba54-1b8cf1da8bf5 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Masked autoencoders are scalable vision learners
Reference 29
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Observation d9e38dd5-255e-43f8-9b2e-a2644d9d9d6e · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Batch normalization: Accelerating deep network training by reducing internal co- variate shift
Reference 30
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Observation ab4956a4-b75c-4262-9fc3-472c09d31966 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Multi- modal contrastive masked autoencoders: A two-stage pro- gressive pre-training approach for rgbd datasets
Reference 31
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Observation 89daa5fe-a322-4714-9eb6-23b9d67c3994 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Adam: A Method for Stochastic Optimization
Reference 32
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Observation f223f396-dc40-441a-8b55-16999915130d · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Global-local self- distillation for visual representation learning
Reference 33
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Observation 5da9c6df-6d17-4d57-9a87-95473c5cdf6c · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Compressive visual representations
Reference 34
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Observation a43acdeb-2b1f-4f77-943d-9ecbc3cfaee4 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Continuous control with deep reinforcement learning
Reference 35
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Observation e131c040-a030-42cd-8831-cc3d5df43a8b · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Ms2l: Multi-task self-supervised learning for skeleton based action recognition
Reference 36
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Observation 2ec36a3d-a925-44cd-b328-3e92a3d59983 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective A closer look at benchmarking self-supervised pre- training with image classification.IJCV, pages 1–13, 2025
Reference 37
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Observation b1badd86-c66f-4e74-aebe-fb5b0eb1959d · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Object-Aware Cropping for Self-Supervised Learning
Reference 38
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Observation a7f06c31-d825-4a5c-bdb8-1444afa4f736 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective An embedding-dynamic approach to self-supervised learning
Reference 39
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Observation 85dcd6f6-9cf9-49e5-a2d1-25872dc4f16e · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Augmentations vs Algorithms: What Works in Self-Supervised Learning
Reference 40
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Observation b04af936-3c23-40e9-acd2-9c5d0799daeb · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective You don’t need domain-specific data augmentations when scaling self- supervised learning.NeurIPS, 37:116106–116125, 2024
Reference 41
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Observation 56bfd40e-8c02-469d-b323-a344253869f7 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Unsupervised learning of visual representations by solving jigsaw puzzles
Reference 42
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Observation b112386e-c9e1-4426-87d2-c8950549b7d1 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective DINOv2: Learning Robust Visual Features without Supervision
Reference 43
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Observation da4c1f45-40e5-4914-9892-eade9883fc86 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Context encoders: Feature learning by inpainting
Reference 44
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Observation 45d74fc1-e692-4531-9bf6-9e0d09e40b28 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective DINOv3
Reference 45
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Observation 884712df-1b5e-41ff-a032-9e8e5c2e4ca6 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Branching out for better byol
Reference 46
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Observation 2a312144-2847-4198-859b-d060d4160fab · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Un- derstanding self-supervised learning dynamics without con- trastive pairs
Reference 47
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Observation c55e718b-856c-41fe-89ce-bd215d1c1cb5 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Unresolved cited work
Reference 48
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Observation 6bf2b598-5114-4e3d-ac84-cad908f0c441 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Training data-efficient image transformers & distillation through at- tention
Reference 49
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Observation c4776db6-b9a7-4e87-852f-f40402a0be3a · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective How to train state-of-the-art mod- els using torchvision’s latest primitives.https : / / pytorch
Reference 50
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Observation f55abdfd-c784-4856-bb2e-4efc3ff4751e · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Adaptive multi-head contrastive learning
Reference 51
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Observation bf4de393-9da0-4d09-8663-e09cf462e6f8 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Dense contrastive learning for self-supervised visual pre-training
Reference 52
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Observation f349a331-668b-4557-9e65-ff9eaeb0407f · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective On the importance of asymmetry for siamese representation learning
Reference 53
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Observation bfd407e5-d1a1-4832-9be6-a7073e87afe9 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Region similarity representation learn- ing
Reference 54
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Observation 736fec50-b27f-47f0-b42b-06123621881b · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Detco: Unsu- pervised contrastive learning for object detection
Reference 55
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Observation a80c1cd7-77fb-4f4b-bebb-73cd1fe46b85 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Simmim: A simple framework for masked image modeling
Reference 56
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Observation fe27d41b-fefe-4822-9f35-46d066295ef8 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Comae: Single model hybrid pre-training on small-scale rgb- d datasets
Reference 57
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Observation 458c65d6-4e3f-4e86-bf90-5147e89a4716 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Large Batch Training of Convolutional Networks
Reference 58
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Observation e2cd105a-6570-418f-9e15-88d7372489e9 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Cutmix: Regu- larization strategy to train strong classifiers with localizable features
Reference 59
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Observation d1ef2950-34aa-4a87-a1ad-374e5885cd60 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Barlow twins: Self-supervised learning via redundancy reduction
Reference 60
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Observation 92c54570-6819-411e-a437-07162ee533a1 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective S4l: Self-supervised semi-supervised learning
Reference 61
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Observation b246056c-b931-42dc-b94a-1c3f6cb1ad93 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective mixup: Beyond Empirical Risk Minimization
Reference 62
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Observation 0d7fe0f1-8e5d-451f-bacb-30472e11effb · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Color- ful image colorization
Reference 63
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Observation 90d405a1-bcb4-46a6-afa0-aa6837f444f2 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Leverage your local and global represen- tations: A new self-supervised learning strategy
Reference 64
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Observation 51eefda9-7d72-4b29-86e5-d3487d084912 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Random erasing data augmentation
Reference 65
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Observation 021c2c9e-5cfa-41c1-8564-1fb0d56cdc5b · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Image bert pre-training with online tokenizer
Reference 66
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Observation 72ea700e-2957-402f-a43f-396276fc0e8f · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective For SimSiam and MoCo v3, the minimum area is set to 20%
Reference 67
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Observation 4cb6d31c-0425-4fc5-bbea-fab7161109bc · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Unresolved cited work
Reference 68
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Observation dc1faf3e-df39-4f85-9bca-90c02087833e · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective BYOL and MoCo v3 use the ranges(0.4,0.4,0.2,0.1), while SimSiam uses (0.4,0.4,0.4,0.1)
Reference 69
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Observation ef64215b-6f2f-44c2-850c-2e143a9c860f · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Unresolved cited work
Reference 70
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Observation de967187-4a60-4fb5-8532-256949db0278 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective In BYOL and MoCo v3, the transformation is applied with 100% probability for the first view and 10% for the second; in SimSiam, both views use a 50% probability
Reference 71
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Observation 525cf03a-a047-48e4-9f27-314dc087e418 · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Unresolved cited work
Reference 72
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Observation bd664a81-1c4b-498f-a502-69a338a5715f · outbound
Self-Supervised Learning with a Multi-Task Latent Space Objective Multi-predictor multi-crop and multi-task strategies
Reference 73
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