Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:40:29.663537Z
Paper Citation Record · LEDGER
As of 11 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2501.01025.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:40:29.663537Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
61 of 61 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 1541da85-4963-441b-b284-76a4aa251080 · outbound
Towards Adversarially Robust Deep Metric Learning , " * write output.state after.block = add.period write newline
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Reference 5
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Towards Adversarially Robust Deep Metric Learning a ckinger, E.; and Shah, R. 1994. Signature verification using a
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Towards Adversarially Robust Deep Metric Learning Towards Evaluating the Robustness of Neural Networks
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Towards Adversarially Robust Deep Metric Learning ALMN: Deep Embedding Learning with Geometrical Virtual Point Generating
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Reference 12
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Reference 13
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Towards Adversarially Robust Deep Metric Learning K.; Harandi, M.; and Sekhar, C
Reference 14
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Towards Adversarially Robust Deep Metric Learning Explaining and Harnessing Adversarial Examples
Reference 15
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Towards Adversarially Robust Deep Metric Learning Countering Adversarial Images using Input Transformations
Reference 16
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Reference 20
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Reference 21
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Reference 22
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Towards Adversarially Robust Deep Metric Learning Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Reference 23
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Towards Adversarially Robust Deep Metric Learning Proxy Anchor Loss for Deep Metric Learning
Reference 24
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Reference 25
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Towards Adversarially Robust Deep Metric Learning Adam: A Method for Stochastic Optimization
Reference 26
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Reference 27
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Towards Adversarially Robust Deep Metric Learning Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy
Reference 28
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Reference 29
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Reference 30
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Observation e56bbeed-a80a-4cea-91d3-6caa1baea664 · outbound
Towards Adversarially Robust Deep Metric Learning Decoupled Weight Decay Regularization
Reference 31
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Reference 32
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Towards Adversarially Robust Deep Metric Learning Towards Deep Learning Models Resistant to Adversarial Attacks
Reference 33
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Observation 0e2b311a-7e0a-44d1-9bb2-169141ca8b67 · outbound
Towards Adversarially Robust Deep Metric Learning K.; Ioffe, S.; and Singh, S
Reference 34
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Reference 35
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Reference 36
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Reference 37
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Reference 38
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Towards Adversarially Robust Deep Metric Learning The Limitations of Deep Learning in Adversarial Settings
Reference 39
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Reference 40
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Reference 41
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Towards Adversarially Robust Deep Metric Learning SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
Reference 42
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Reference 44
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Reference 45
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Towards Adversarially Robust Deep Metric Learning Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Reference 46
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Towards Adversarially Robust Deep Metric Learning MobileNetV2: Inverted Residuals and Linear Bottlenecks
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Reference 48
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Towards Adversarially Robust Deep Metric Learning Constrained Deep Metric Learning for Person Re-identification
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Towards Adversarially Robust Deep Metric Learning H.; and Hospedales, T
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Towards Adversarially Robust Deep Metric Learning Intriguing properties of neural networks
Reference 51
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Towards Adversarially Robust Deep Metric Learning Manifold Mixup: Better Representations by Interpolating Hidden States
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Reference 53
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Reference 54
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Reference 55
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Reference 56
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Reference 57
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Reference 58
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Reference 59
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Reference 60
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Reference 61
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