Paper Citation Record · LEDGER

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation

As of 7 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.08686.

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

pith.paper-citation-record.v1
2507.08686 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

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measured 39 of 39 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation cbaff13d-80c1-43f4-b9cd-7742953da9a2 · outbound

This paper cites A convnet for the 2020s,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation A convnet for the 2020s,

Reference 1

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Observation 4fb93a48-7349-4b49-aaba-2c382fff0e0b · outbound

This paper cites Deep learning-based improved snapshot ensemble technique for covid-19 chest x-ray classification,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Deep learning-based improved snapshot ensemble technique for covid-19 chest x-ray classification,

Reference 2

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Observation ef575f4d-95d0-483c-a06f-34fdb484adf5 · outbound

This paper cites On Local Overfitting and Forgetting in Deep Neural Networks.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation On Local Overfitting and Forgetting in Deep Neural Networks

Reference 3

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Observation 22ce29d4-44a2-47ab-87f1-52fc3c7c4eb3 · outbound

This paper cites A closer look at memorization in deep networks,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation A closer look at memorization in deep networks,

Reference 4

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Observation 3600a677-4c6e-46ba-815b-21e537e0048d · outbound

This paper cites Catastrophic interfer- ence in connectionist networks: The sequential learning problem,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Catastrophic interfer- ence in connectionist networks: The sequential learning problem,

Reference 5

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Observation ee681d88-199a-4121-823d-57f890f3b47e · outbound

This paper cites A survey on ensemble learning under the era of deep learning,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation A survey on ensemble learning under the era of deep learning,

Reference 6

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Observation fd565a2f-ec28-47de-b1a7-b4d36da0805c · outbound

This paper cites Dropout: a simple way to pre- vent neural networks from overfitting,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Dropout: a simple way to pre- vent neural networks from overfitting,

Reference 7

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Observation e0d036fa-b036-4138-b402-152f030c9c7f · outbound

This paper cites Horizontal and Vertical Ensemble with Deep Representation for Classification.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Horizontal and Vertical Ensemble with Deep Representation for Classification

Reference 8

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This paper cites Acceleration of stochastic approximation by averaging,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Acceleration of stochastic approximation by averaging,

Reference 9

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Observation 5fbddbde-fc22-4141-9490-04045e6d8783 · outbound

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

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Averaging Weights Leads to Wider Optima and Better Generalization

Reference 10

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Observation 60d0deda-d632-4dc3-8e54-5cf8d40dbfd4 · outbound

This paper cites dropcyclic: snapshot en- semble convolutional neural network based on a new learning rate schedule for land use classification,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation dropcyclic: snapshot en- semble convolutional neural network based on a new learning rate schedule for land use classification,

Reference 11

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Observation 197f8b4a-c9c5-418b-9e1a-ca3086de1d1b · outbound

This paper cites Stochastic weight averaging revisited,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Stochastic weight averaging revisited,

Reference 12

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Observation dd0a1ef2-6aa9-43c4-9470-ce09b60ee3f3 · outbound

This paper cites Reconcil- ing modern machine-learning practice and the classical bias–variance trade-off,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Reconcil- ing modern machine-learning practice and the classical bias–variance trade-off,

Reference 13

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This paper cites Deep double descent: Where bigger models and more data hurt,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Deep double descent: Where bigger models and more data hurt,

Reference 14

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Observation 253ab1ba-467c-4da7-b099-75b03d3f0035 · outbound

This paper cites When and how epochwise double descent happens.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation When and how epochwise double descent happens

Reference 15

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Observation 078eaf52-f203-40d6-b116-55c7f5b41b16 · outbound

This paper cites Early Stopping in Deep Networks: Double Descent and How to Eliminate it.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Early Stopping in Deep Networks: Double Descent and How to Eliminate it

Reference 16

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Observation 94f07cc1-5eb5-4e3e-be6c-9b50fd1d5d6d · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Distilling the Knowledge in a Neural Network

Reference 17

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Observation 38b0fb14-64cb-4d9c-97f0-a07951a89ed5 · outbound

This paper cites Rethinking Self-Distillation: Label Averaging and Enhanced Soft Label Refinement with Partial Labels.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Rethinking Self-Distillation: Label Averaging and Enhanced Soft Label Refinement with Partial Labels

Reference 18

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This paper cites United we stand: Using epoch-wise agreement of ensembles to combat overfit,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation United we stand: Using epoch-wise agreement of ensembles to combat overfit,

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Observation c799125e-ab5f-436e-9539-60df4ff2888d · outbound

This paper cites Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

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This paper cites Revisiting Self-Distillation.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Revisiting Self-Distillation

Reference 21

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Observation d9c93570-d932-4a75-98b9-130d2b34b6a1 · outbound

This paper cites Understanding self-distillation in the presence of label noise,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Understanding self-distillation in the presence of label noise,

Reference 22

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Observation 26ebbe31-060c-4542-83ba-9a33cca7750e · outbound

This paper cites Efficient knowledge distillation from model check- points,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Efficient knowledge distillation from model check- points,

Reference 23

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Observation feaa9914-09bb-4b7a-9d16-80f5663cb750 · outbound

This paper cites Learn from the past: Experience ensemble knowledge distilla- tion,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Learn from the past: Experience ensemble knowledge distilla- tion,

Reference 24

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Observation 2f82c8b5-67a2-4cdd-9ee8-4092e3511575 · outbound

This paper cites Snapshot Ensembles: Train 1, get M for free.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Snapshot Ensembles: Train 1, get M for free

Reference 25

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This paper cites Loss surfaces, mode connectivity, 1 and fast ensembling of dnns,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Loss surfaces, mode connectivity, 1 and fast ensembling of dnns,

Reference 26

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 27

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Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Maxvit: Multi-axis vision trans- former,

Reference 28

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Observation b867ce83-b17b-47e5-90f9-cc5e1de861dd · outbound

This paper cites Principal components bias in over-parameterized linear models, and its mani- festation in deep neural networks,.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Principal components bias in over-parameterized linear models, and its mani- festation in deep neural networks,

Reference 29

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Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Imagenet: A large-scale hierarchical image database,

Reference 30

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Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Learning multiple layers of features from tiny images,

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Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Tiny imagenet visual recognition challenge,

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Observation b6d2f36f-93a3-417b-8e99-544e444bf8c8 · outbound

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Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Learning with noisy labels revisited: A study using real-world human annotations,

Reference 33

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Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Deep residual learning for image recognition,

Reference 34

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Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Making deep neural networks robust to label noise: A loss correction approach,

Reference 35

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Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Towards fair- ness in visual recognition: Effective strategies for bias mitigation,

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Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

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Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Unresolved cited work

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