Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation | Paper Citation Record

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This paper cites Learning to transfer examples for partial domain adaptation.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Learning to transfer examples for partial domain adaptation

Reference 1

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Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Unresolved cited work

Reference 2

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This paper cites A continual learning survey: Defying for- getting in classification tasks.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation A continual learning survey: Defying for- getting in classification tasks

Reference 3

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

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 4

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This paper cites Caltech-256 object category dataset, 2007.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Caltech-256 object category dataset, 2007

Reference 5

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This paper cites Gradient reweighting: Towards imbalanced class-incremental learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Gradient reweighting: Towards imbalanced class-incremental learning

Reference 6

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This paper cites Class-incremental learning with clip: Adaptive representa- tion adjustment and parameter fusion.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Class-incremental learning with clip: Adaptive representa- tion adjustment and parameter fusion

Reference 7

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This paper cites OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning

Reference 8

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This paper cites Improved cross-corpus speech emotion recognition using deep local domain adaptation.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Improved cross-corpus speech emotion recognition using deep local domain adaptation

Reference 9

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This paper cites Class- incremental learning by knowledge distillation with adaptive feature consolidation.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Class- incremental learning by knowledge distillation with adaptive feature consolidation

Reference 10

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This paper cites C-sfda: A curriculum learning aided self- training framework for efficient source free domain adapta- tion.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation C-sfda: A curriculum learning aided self- training framework for efficient source free domain adapta- tion

Reference 11

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This paper cites Supervised contrastive learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Supervised contrastive learning

Reference 12

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This paper cites Class- incremental domain adaptation.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Class- incremental domain adaptation

Reference 13

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This paper cites Effective decision boundary learning for class incremen- tal learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Effective decision boundary learning for class incremen- tal learning

Reference 14

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This paper cites Principal properties attention matching for par- tial domain adaptation in fault diagnosis.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Principal properties attention matching for par- tial domain adaptation in fault diagnosis

Reference 15

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This paper cites Prototype- guided continual adaptation for class-incremental unsuper- vised domain adaptation.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Prototype- guided continual adaptation for class-incremental unsuper- vised domain adaptation

Reference 16

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This paper cites Class Incremental Learning via Likelihood Ratio Based Task Prediction.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Class Incremental Learning via Likelihood Ratio Based Task Prediction

Reference 17

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This paper cites Guid- ing pseudo-labels with uncertainty estimation for source- free unsupervised domain adaptation.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Guid- ing pseudo-labels with uncertainty estimation for source- free unsupervised domain adaptation

Reference 18

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This paper cites Exploiting Fine-Grained Prototype Distribution for Boosting Unsupervised Class Incremental Learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Exploiting Fine-Grained Prototype Distribution for Boosting Unsupervised Class Incremental Learning

Reference 19

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This paper cites Entity-enhanced adaptive re- construction network for weakly supervised referring expres- sion grounding.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Entity-enhanced adaptive re- construction network for weakly supervised referring expres- sion grounding

Reference 20

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This paper cites Model behavior preserving for class-incremental learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Model behavior preserving for class-incremental learning

Reference 21

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This paper cites Source-free domain adap- tation with domain generalized pretraining for face anti- spoofing.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Source-free domain adap- tation with domain generalized pretraining for face anti- spoofing

Reference 22

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This paper cites Diffclass: Diffusion-based class incremental learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Diffclass: Diffusion-based class incremental learning

Reference 23

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This paper cites Un- derstanding and improving source-free domain adaptation from a theoretical perspective.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Un- derstanding and improving source-free domain adaptation from a theoretical perspective

Reference 24

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This paper cites Efficient test-time model adaptation without forgetting.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Efficient test-time model adaptation without forgetting

Reference 25

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This paper cites Bmd: A general class-balanced multicen- tric dynamic prototype strategy for source-free domain adap- tation.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Bmd: A general class-balanced multicen- tric dynamic prototype strategy for source-free domain adap- tation

Reference 26

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This paper cites Upcycling models under domain and category shift.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Upcycling models under domain and category shift

Reference 27

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This paper cites Lead: Learn- ing decomposition for source-free universal domain adapta- tion.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Lead: Learn- ing decomposition for source-free universal domain adapta- tion

Reference 28

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This paper cites Fscil-eaca: Few-shot class-incremental learning network based on em- bedding augmentation and classifier adaptation for image classification.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Fscil-eaca: Few-shot class-incremental learning network based on em- bedding augmentation and classifier adaptation for image classification

Reference 29

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This paper cites Imagenet large scale visual recognition challenge.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Imagenet large scale visual recognition challenge

Reference 30

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This paper cites Adapting visual category models to new domains.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Adapting visual category models to new domains

Reference 31

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This paper cites A survey on image data augmentation for deep learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation A survey on image data augmentation for deep learning

Reference 32

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This paper cites Source-free domain adaptation via target prediction distribution searching.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Source-free domain adaptation via target prediction distribution searching

Reference 33

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Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Unified source-free domain adaptation

Reference 34

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This paper cites Source- free domain adaptation with frozen multimodal foundation model, 2024.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Source- free domain adaptation with frozen multimodal foundation model, 2024

Reference 35

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This paper cites A prototype-oriented framework for unsupervised domain adaptation.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation A prototype-oriented framework for unsupervised domain adaptation

Reference 36

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This paper cites Topology-preserving class-incremental learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Topology-preserving class-incremental learning

Reference 37

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This paper cites Transformer-based under- sampled single-pixel imaging.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Transformer-based under- sampled single-pixel imaging

Reference 38

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This paper cites SMART: Syntax-Calibrated Multi-Aspect Relation Transformer for Change Captioning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation SMART: Syntax-Calibrated Multi-Aspect Relation Transformer for Change Captioning

Reference 39

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This paper cites Deep hashing network for unsupervised domain adaptation.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Deep hashing network for unsupervised domain adaptation

Reference 40

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This paper cites Large scale incre- mental learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Large scale incre- mental learning

Reference 41

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This paper cites Unraveling the mysteries of label noise in source-free domain adaptation: Theory and practice.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Unraveling the mysteries of label noise in source-free domain adaptation: Theory and practice

Reference 42

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This paper cites Unsupervised cross-media hashing learning via knowledge graph.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Unsupervised cross-media hashing learning via knowledge graph

Reference 43

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This paper cites Inductive state- relabeling adversarial active learning with heuristic clique rescaling.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Inductive state- relabeling adversarial active learning with heuristic clique rescaling

Reference 44

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This paper cites Monocular depth estimation on adverse weath- ers with curriculum domain distribution alignment.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Monocular depth estimation on adverse weath- ers with curriculum domain distribution alignment

Reference 45

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Observation 509a1f35-e6aa-46c3-bb40-86be1b1a85b8 · outbound

This paper cites Deep guided attention network for joint denoising and demosaicing in real image.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Deep guided attention network for joint denoising and demosaicing in real image

Reference 46

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Observation 440df2c9-1e1f-4011-b29d-3f8591cff6f0 · outbound

This paper cites From speaker to dubber: movie dubbing with prosody and duration consistency learning.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation From speaker to dubber: movie dubbing with prosody and duration consistency learning

Reference 47

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This paper cites Comparing proba- bility distributions with conditional transport.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Comparing proba- bility distributions with conditional transport

Reference 48

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This paper cites Gait recognition in the wild with dense 3d representations and A benchmark.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Gait recognition in the wild with dense 3d representations and A benchmark

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:40:53.328080Z

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Observation 3f3c0051-326d-4b3b-9256-5a0cd992741d · outbound

This paper cites Source-free domain adaptation with class prototype discovery.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Source-free domain adaptation with class prototype discovery

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:40:53.298396Z

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Observation 51df04b4-7f93-43a2-bf24-41bad207ddea · outbound

This paper cites Unsupervised domain adaption harnessing vision-language pre-training.

Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation Unsupervised domain adaption harnessing vision-language pre-training

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:40:53.277572Z

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