Paper Citation Record · LEDGER

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes

As of 15 August 2026, this Paper Citation Record lists 86 of 86 outbound references and 3 inbound Pith citation observations for arXiv:2412.13573.

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

pith.paper-citation-record.v1
2412.13573 v2

Coverage vector

measured 86 of 86 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:06:17.047011Z

measured 89 of 89 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:56:15.655803Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:08:55.762533Z

Reference resolution

86 of 86 outbound references displayed

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External citation measurements

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Outbound references

Observation 2de3ec4c-0132-4b20-bb0c-9b9988551093 · outbound

This paper cites Invariant Risk Minimization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Invariant Risk Minimization

Reference 1

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Observation 6e541882-1b0f-4466-8b5c-3be748282e2d · outbound

This paper cites Sharpness-Aware Minimization Improves Language Model Generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Sharpness-Aware Minimization Improves Language Model Generalization

Reference 2

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Observation 7d4f9e9c-f420-42fc-9787-328e6623fe8f · outbound

This paper cites Decaug: Out-of-distribution generalization via decomposed feature representation and semantic augmentation.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Decaug: Out-of-distribution generalization via decomposed feature representation and semantic augmentation

Reference 3

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This paper cites Nas-ood: Neural ar- chitecture search for out-of-distribution generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Nas-ood: Neural ar- chitecture search for out-of-distribution generalization

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This paper cites Metareg: Towards domain generalization using meta- regularization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Metareg: Towards domain generalization using meta- regularization

Reference 5

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This paper cites Recognition in terra incognita.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Recognition in terra incognita

Reference 6

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Observation 8108ed33-7376-4bab-af60-09ca7705de8c · outbound

This paper cites Domain generalization by marginal transfer learning.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Domain generalization by marginal transfer learning

Reference 7

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Observation c286735e-5b65-42af-92c1-8da3d4459ca9 · outbound

This paper cites Ex- ploiting domain-specific features to enhance domain gener- alization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Ex- ploiting domain-specific features to enhance domain gener- alization

Reference 8

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Observation d4b5ee9c-10f9-41c0-aabf-0ee791f2ad1f · outbound

This paper cites Swad: Domain generalization by seeking flat minima.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Swad: Domain generalization by seeking flat minima

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This paper cites Domain Generalization by Mutual-Information Regularization with Pre-trained Models.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Domain Generalization by Mutual-Information Regularization with Pre-trained Models

Reference 10

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Observation 116246c2-c9e3-4a2f-849c-b8f987b90328 · outbound

This paper cites Entropy-sgd: Bias- ing gradient descent into wide valleys.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Entropy-sgd: Bias- ing gradient descent into wide valleys

Reference 11

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This paper cites Sharpness-aware training for free.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Sharpness-aware training for free

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This paper cites Learning to learn with variational information bottleneck for domain general- ization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Learning to learn with variational information bottleneck for domain general- ization

Reference 13

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This paper cites Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Reference 14

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This paper cites Unbiased met- ric learning: On the utilization of multiple datasets and web images for softening bias.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Unbiased met- ric learning: On the utilization of multiple datasets and web images for softening bias

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This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Sharpness-Aware Minimization for Efficiently Improving Generalization

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This paper cites Domain-adversarial train- ing of neural networks.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Domain-adversarial train- ing of neural networks

Reference 17

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Observation f904ab90-b01a-4826-adde-b9d9d28ba7f3 · outbound

This paper cites Are Vision Transformers Robust to Spurious Correlations?.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Are Vision Transformers Robust to Spurious Correlations?

Reference 18

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This paper cites In Search of Lost Domain Generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes In Search of Lost Domain Generalization

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Simplifying neu- ral nets by discovering flat minima

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Flat minima

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This paper cites Self-challenging improves cross-domain generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Self-challenging improves cross-domain generalization

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This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Averaging Weights Leads to Wider Optima and Better Generalization

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes A single-step, sharpness- aware minimization is all you need to achieve efficient and accurate sparse training

Reference 24

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Visual Prompt Tuning

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes An Adaptive Policy to Employ Sharpness-Aware Minimization

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This paper cites Fantastic Generalization Measures and Where to Find Them.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Fantastic Generalization Measures and Where to Find Them

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This paper cites When do flat minima optimizers work? Advances in Neural Information Processing Systems , 35:16577–16595,.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes When do flat minima optimizers work? Advances in Neural Information Processing Systems , 35:16577–16595,

Reference 28

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Deep learn- ing for NLP and speech recognition

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Selfreg: Self-supervised contrastive regu- larization for domain generalization

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Adam: A Method for Stochastic Optimization

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Out-of-distribution general- ization via risk extrapolation (rex)

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This paper cites Learning common and specific visual prompts for domain generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Learning common and specific visual prompts for domain generalization

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Invariant informa- tion bottleneck for domain generalization

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Deeper, broader and artier domain generaliza- tion

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Learning to generalize: Meta-learning for do- main generalization

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Domain generalization with adversarial feature learning

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Visualizing the loss landscape of neural nets.Ad- vances in neural information processing systems , 31, 2018

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Domain generalization via conditional invari- ant representations

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Adapting neural architectures between domains

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Internal Consistency and Self-Feedback in Large Language Models: A Survey

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Deep Learning applied to NLP

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Self-refine: It- erative refinement with self-feedback

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Training Recurrent Neural Networks by Diffusion

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes When does label smoothing help? Advances in neural infor- mation processing systems, 32, 2019

Reference 46

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Reducing Domain Gap by Reducing Style Bias

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Learning explanations that are hard to vary

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Moment matching for multi-source domain adaptation

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Learn- ing transferable visual models from natural language super- vision

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

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Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Gradient Matching for Domain Generalization

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This paper cites Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing

Reference 53

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Observation b0a688ea-97f0-4aaa-ba90-32f743186652 · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Deep coral: Correlation alignment for deep domain adaptation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.721337Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d37afa9b-1c07-4bd7-bb41-3f14505f3e9a · outbound

This paper cites Statistical learning theory.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Statistical learning theory

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.707226Z

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Observation a2c8f9cd-d777-40e7-8cfb-c3db678b2f92 · outbound

This paper cites Deep hashing network for 10 unsupervised domain adaptation.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Deep hashing network for 10 unsupervised domain adaptation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.694424Z

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Observation 4cc5e32a-4194-4289-8f92-eed7f3547e25 · outbound

This paper cites Generalizing to unseen domains via adversarial data augmentation.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Generalizing to unseen domains via adversarial data augmentation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.681986Z

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Observation 7737817b-25ca-4cd5-8b6b-414a119cfac9 · outbound

This paper cites Deep learning for computer vision: A brief review.Computational intelligence and neuroscience, 2018, 2018.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Deep learning for computer vision: A brief review.Computational intelligence and neuroscience, 2018, 2018

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.670059Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2fcef182-687c-45b8-9aa3-ed33de67dbfd · outbound

This paper cites Generalizing to unseen domains: A survey on domain generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Generalizing to unseen domains: A survey on domain generalization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.657023Z

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Observation 7f8ba0a6-f742-453b-9f74-62477db6215e · outbound

This paper cites Deep visual domain adapta- tion: A survey.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Deep visual domain adapta- tion: A survey

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.644100Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 20cab76c-517c-467d-9b79-e7797a5e9fcc · outbound

This paper cites Sharpness-aware gradient matching for domain generaliza- tion.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Sharpness-aware gradient matching for domain generaliza- tion

Reference 61

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Observation 11f52561-6868-487f-894b-707fc76e3618 · outbound

This paper cites Neural Architecture Search: Insights from 1000 Papers.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Neural Architecture Search: Insights from 1000 Papers

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:16.948572Z

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Observation 3e0ff5a9-429f-4bcf-b519-06ffa773ce72 · outbound

This paper cites Delving deep into the gener- alization of vision transformers under distribution shifts.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Delving deep into the gener- alization of vision transformers under distribution shifts

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.625344Z

Source-reported events for the cited work

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Observation 8ef451c8-c562-4fa1-bbb6-22f759c55c31 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes mixup: Beyond Empirical Risk Minimization

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:16.957026Z

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Observation 8780e5dd-9385-46ce-9615-64663f4ccc87 · outbound

This paper cites Adaptive Risk Minimization: Learning to Adapt to Domain Shift.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Adaptive Risk Minimization: Learning to Adapt to Domain Shift

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:16.960845Z

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Observation 41038aee-3566-414d-b735-031091ddef26 · outbound

This paper cites Deep stable learning for out-of- distribution generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Deep stable learning for out-of- distribution generalization

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.613368Z

Source-reported events for the cited work

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Observation 325e57cd-6fbe-4f5d-bd6a-200b02e13414 · outbound

This paper cites Flatness-aware minimization for domain generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Flatness-aware minimization for domain generalization

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.601743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 647912d7-9d4b-49b2-b346-934b478a8bf3 · outbound

This paper cites Gradient norm aware minimization seeks first-order flatness and improves generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Gradient norm aware minimization seeks first-order flatness and improves generalization

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.590320Z

Source-reported events for the cited work

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Observation 78340a86-199b-4c9c-a928-6add4c2453d9 · outbound

This paper cites Deep learning for environmentally robust speech recognition: An overview of recent developments.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Deep learning for environmentally robust speech recognition: An overview of recent developments

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.579237Z

Source-reported events for the cited work

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Observation db101a5d-e22a-4873-a5b5-8d2e04503c15 · outbound

This paper cites Prompt Vision Transformer for Domain Generalization.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Prompt Vision Transformer for Domain Generalization

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:16.980248Z

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Observation 8edb5e9f-a1dc-4b3b-a70c-d50386cdd7fa · outbound

This paper cites Domain Generalization with MixStyle.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Domain Generalization with MixStyle

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:16.984194Z

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Observation 5069b185-10d9-4218-bf7b-4ca8f29e972f · outbound

This paper cites Surrogate Gap Minimization Improves Sharpness-Aware Training.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Surrogate Gap Minimization Improves Sharpness-Aware Training

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:16.988331Z

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Observation d7476d40-caed-4477-aed9-4c5a7e2726f2 · outbound

This paper cites Towards Robust Out-of-Distribution Generalization Bounds via Sharpness.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Towards Robust Out-of-Distribution Generalization Bounds via Sharpness

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T13:06:16.992525Z

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Observation c9f95dd8-5043-4059-9bd0-b979baf7149a · outbound

This paper cites Pac-bayesian model averaging.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Pac-bayesian model averaging

Reference 74

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T13:06:17.568273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3dcb2cc8-69fb-4069-8037-9992b8bb4083 · outbound

This paper cites For clarity and ease of understanding, we first pro- vide a detailed explanation of the relevant notations and concepts that will be used throughout the analysis.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes For clarity and ease of understanding, we first pro- vide a detailed explanation of the relevant notations and concepts that will be used throughout the analysis

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.556870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 73a0f792-b594-4034-940e-b35eafccbad6 · outbound

This paper cites An efficient algorithm (Alogrithm 2) has been presented to address the associated KL divergence minimization problem there.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes An efficient algorithm (Alogrithm 2) has been presented to address the associated KL divergence minimization problem there

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.544704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9ad8dfc4-3ae6-4788-b11d-7240295a2975 · outbound

This paper cites The stationarity conditions require that the partial derivatives of the Lagrangian with respect to each of the variables be zero, which corresponds to the opti- mality condition.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes The stationarity conditions require that the partial derivatives of the Lagrangian with respect to each of the variables be zero, which corresponds to the opti- mality condition

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.533077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4db4d20c-aaa0-4b75-96a4-54a8832b5d18 · outbound

This paper cites The primal feasibility condition en- sures that the original constraints are satisfied.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes The primal feasibility condition en- sures that the original constraints are satisfied

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.521382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b20bd0fe-2eaf-4959-8139-3fb5ae85ece3 · outbound

This paper cites The dual feasibility condition imposes non-negativity on the Lagrange multipliers associated with the inequality constraints: µj ≥ 0.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes The dual feasibility condition imposes non-negativity on the Lagrange multipliers associated with the inequality constraints: µj ≥ 0

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.508708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b428cc3f-37e5-4a31-adf5-72b29812e257 · outbound

This paper cites Finally, the complementary slackness condition relates the primal and dual variables.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Finally, the complementary slackness condition relates the primal and dual variables

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:06:17.495610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8e65af09-fca0-4d6e-b237-c9d30c54f900 · outbound

This paper cites an unresolved cited work.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-11T13:06:17.483951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 327a1e89-5464-4bfb-bdbd-bdffd9ad4e5b · outbound

This paper cites , jt−1} ⊆A, if the inequality (pα 1 ( Y j∈C αpj)) 1 |C|+α < αpjt (44) holds, then jt ∈ A.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes , jt−1} ⊆A, if the inequality (pα 1 ( Y j∈C αpj)) 1 |C|+α < αpjt (44) holds, then jt ∈ A

Reference 82

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T13:06:17.472249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd6d9af9-8539-40da-b3e8-006e8f8bb310 · outbound

This paper cites These experiments were conducted using ResNet-50, which was pre-trained on ImageNet.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes These experiments were conducted using ResNet-50, which was pre-trained on ImageNet

Reference 83

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T13:06:17.460287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fd299bbf-d0cb-467d-832f-46d7ac53cfd7 · outbound

This paper cites an unresolved cited work.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-11T13:06:17.447966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 612f9224-7eca-4954-b3e0-fd27570269da · outbound

This paper cites an unresolved cited work.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Unresolved cited work

Reference 85

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T13:06:17.435405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a4c3377c-6f70-49fa-9204-7ca29f907b49 · outbound

This paper cites an unresolved cited work.

Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-11T13:06:17.423245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation b49071f0-387a-4158-a707-ba9ba70341be · inbound

Harmonizing and Merging Source Models for CLIP-based Domain Generalization cites this paper.

Harmonizing and Merging Source Models for CLIP-based Domain Generalization Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes

Reference 6

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Observation cf329a04-60a2-437c-8de6-77e56504f203 · inbound

A Risk Decomposition Framework for Pre-Hoc Fine-Tuning Prediction cites this paper.

A Risk Decomposition Framework for Pre-Hoc Fine-Tuning Prediction Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-07-03T19:28:52.342481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f7511c21-f0b5-4547-9aba-43608fe4b077 · inbound

TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins cites this paper.

TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:08:55.764911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.