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Paper Citation Record · LEDGER

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts

As of 7 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2506.04673.

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

pith.paper-citation-record.v1
2506.04673 v1

Coverage vector

measured 46 of 46 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 85c08559-0ce4-4aa3-800a-b11ad1901db7 · outbound

This paper cites This looks like that: deep learning for interpretable image recognition,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts This looks like that: deep learning for interpretable image recognition,

Reference 1

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Observation b7fd64db-9ef2-4593-927e-73da633b46c8 · outbound

This paper cites Learning transferable conceptual prototypes for interpretable unsupervised domain adaptation,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Learning transferable conceptual prototypes for interpretable unsupervised domain adaptation,

Reference 2

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Observation c529a971-6bab-4a3f-98da-d00012edb100 · outbound

This paper cites Describe, spot and explain: Interpretable representation learning for discriminative visual reasoning,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Describe, spot and explain: Interpretable representation learning for discriminative visual reasoning,

Reference 3

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Observation 8282d6e2-9684-4a7c-adcb-0b11fd7bf278 · outbound

This paper cites An interpretable deep architecture for similarity learning built upon hierarchical concepts,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts An interpretable deep architecture for similarity learning built upon hierarchical concepts,

Reference 4

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Observation a2b9e68f-4487-48f6-90ad-8f23457438b8 · outbound

This paper cites Explainability enhanced object detection transformer with feature disentanglement,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Explainability enhanced object detection transformer with feature disentanglement,

Reference 5

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This paper cites Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai,

Reference 6

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Observation 75dfde88-22b9-4b8d-9f30-fdf8348e4ee5 · outbound

This paper cites Prediction with visual evidence: Sketch classification explanation via stroke- level attributions,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Prediction with visual evidence: Sketch classification explanation via stroke- level attributions,

Reference 7

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Observation de790c55-f59d-4a89-a488-19c9c0bbe179 · outbound

This paper cites Joint answering and explanation for visual commonsense reasoning,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Joint answering and explanation for visual commonsense reasoning,

Reference 8

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Observation 808bf448-0649-4f18-ac4d-2c2dadde920a · outbound

This paper cites Interpretable image recognition by constructing transparent embedding space,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Interpretable image recognition by constructing transparent embedding space,

Reference 9

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Observation 0b6c944f-89dc-46af-9e6d-e2ae145f41d6 · outbound

This paper cites A grass- mann manifold handbook: Basic geometry and computational aspects,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts A grass- mann manifold handbook: Basic geometry and computational aspects,

Reference 10

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This paper cites Pip- net: Patch-based intuitive prototypes for interpretable image classification,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Pip- net: Patch-based intuitive prototypes for interpretable image classification,

Reference 11

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This paper cites ProtoS-ViT: Visual foundation models for sparse self-explainable classifications.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts ProtoS-ViT: Visual foundation models for sparse self-explainable classifications

Reference 12

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 13

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Observation 72cdea4c-1a81-4610-9ac1-8266c4f65db1 · outbound

This paper cites Learning to prompt for vision-language models,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Learning to prompt for vision-language models,

Reference 14

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Conditional prompt learning for vision-language models,

Reference 15

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Visual-language prompt tuning with knowledge-guided context optimization,

Reference 16

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Prompt- aligned gradient for prompt tuning,

Reference 17

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Clip-adapter: Better vision-language models with feature adapters,

Reference 18

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 19

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Task resid- ual for tuning vision-language models,

Reference 20

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This paper cites Adaptive mixtures of local experts,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Adaptive mixtures of local experts,

Reference 21

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Hierarchical mixtures of experts and the em algorithm,

Reference 22

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This paper cites Nonlinear models using dirichlet process mixtures,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Nonlinear models using dirichlet process mixtures,

Reference 23

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This paper cites A parallel mixture of svms for very large scale problems,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts A parallel mixture of svms for very large scale problems,

Reference 24

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This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 25

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This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 26

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity,

Reference 27

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Adap- tive gating in mixture-of-experts based language models,

Reference 28

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This paper cites DeepSeekMoE: Towards ultimate expert specialization in mixture-of-experts language models,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts DeepSeekMoE: Towards ultimate expert specialization in mixture-of-experts language models,

Reference 29

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Observation c7a11cf8-b08d-4595-8cab-aedb206cf15c · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Emerging properties in self-supervised vision transformers,

Reference 30

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts DINOv2: Learning Robust Visual Features without Supervision

Reference 31

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This paper cites Lora: Low-rank adaptation of large language models.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Lora: Low-rank adaptation of large language models

Reference 32

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Observation 8032bed9-ec73-4314-81b6-5086ae57b239 · outbound

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Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning

Reference 33

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Observation 947d6bcb-f44f-4a11-b356-3b4999b64496 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts The power of scale for parameter-efficient prompt tuning,

Reference 34

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Source-reported events for the cited work

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Observation df46b3d9-62a7-4d99-b085-a10b91f3ee7e · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Prefix-tuning: Optimizing continuous prompts for generation,

Reference 35

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Observation ee183074-9485-4711-b2b6-6ebe8b71a9fe · outbound

This paper cites Sparse low-rank adaptation of pre-trained language models,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Sparse low-rank adaptation of pre-trained language models,

Reference 36

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 7e09aab7-6876-4ef8-9650-44776a078aeb · outbound

This paper cites Enhancing few-shot out-of-distribution detection with pre- trained model features,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Enhancing few-shot out-of-distribution detection with pre- trained model features,

Reference 37

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation b1197f77-54c7-4ff7-9122-d17d51c45955 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Representation Learning with Contrastive Predictive Coding

Reference 38

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Unavailable: canonical work link unavailable.

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Observation f9c2abec-9852-4876-8a20-7ad98415f389 · outbound

This paper cites The Caltech-UCSD Birds-200-2011 Dataset,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts The Caltech-UCSD Birds-200-2011 Dataset,

Reference 39

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 162dfcd0-dc23-4600-9bb5-0aff491847c3 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Fine-Grained Visual Classification of Aircraft

Reference 40

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 572f2ffa-91f8-48a1-84e6-94ae436f645f · outbound

This paper cites Matching networks for one shot learning,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Matching networks for one shot learning,

Reference 41

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 8f6dcc76-d311-4046-bf2c-a9700e4cf3c4 · outbound

This paper cites Meta-learning with differentiable closed-form solvers.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Meta-learning with differentiable closed-form solvers

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T10:40:28.758971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5e668763-d1ec-4830-9134-df155bdb0b1b · outbound

This paper cites Few-Shot Learning with Metric-Agnostic Conditional Embeddings.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Few-Shot Learning with Metric-Agnostic Conditional Embeddings

Reference 43

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation bdda8ce3-3daf-417f-8521-a7d4448583bd · outbound

This paper cites Describing textures in the wild,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Describing textures in the wild,

Reference 44

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation e91935e0-1d4e-4d0d-aecb-44b58aae2db1 · outbound

This paper cites Evaluation and improvement of interpretability for self-explainable part-prototype networks,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Evaluation and improvement of interpretability for self-explainable part-prototype networks,

Reference 45

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation b0b9cfe8-787b-48a7-bf65-2822cde8bcdc · outbound

This paper cites Learning support and trivial prototypes for interpretable image classification,.

Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts Learning support and trivial prototypes for interpretable image classification,

Reference 46

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Pith citing papers

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