ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation | Paper Citation Record

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This paper cites Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation & Lillicrap, T

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This paper cites Prototypical Networks for Few-shot Learning.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Prototypical Networks for Few-shot Learning

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation & Haffner, P

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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This paper cites Attention Is All You Need.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Attention Is All You Need

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This paper cites CrossTransformers: spatially-aware few-shot transfer.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation CrossTransformers: spatially-aware few-shot transfer

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Meta-learning with differentiable closed-form solvers

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

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation The Caltech-UCSD Birds-200- 2011 Dataset

Reference 9

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This paper cites TADAM: Task dependent adaptive metric for improved few-shot learning.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation TADAM: Task dependent adaptive metric for improved few-shot learning

Reference 10

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This paper cites Learning to Compare: Relation Network for Few-Shot Learning.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Learning to Compare: Relation Network for Few-Shot Learning

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Unresolved cited work

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This paper cites H., Shehata, M.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation H., Shehata, M

Reference 13

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This paper cites Deep Residual Learning for Image Recognition.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Deep Residual Learning for Image Recognition

Reference 14

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Densely Connected Convolutional Networks

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Wide Residual Networks

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation & Drummond, T

Reference 17

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This paper cites S., Chaudhari, P., Ravichandran, A.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation S., Chaudhari, P., Ravichandran, A

Reference 18

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This paper cites On First-Order Meta-Learning Algorithms.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation On First-Order Meta-Learning Algorithms

Reference 19

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This paper cites Meta-SGD: Learning to Learn Quickly for Few-Shot Learning.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

Reference 20

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Meta-Learning with Differentiable Convex Optimization

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This paper cites Meta-Learning for Semi-Supervised Few-Shot Classification.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Meta-Learning for Semi-Supervised Few-Shot Classification

Reference 22

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation & Monfardini, G

Reference 23

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This paper cites Few-Shot Learning with Graph Neural Networks.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Few-Shot Learning with Graph Neural Networks

Reference 24

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation & Salakhutdinov, R

Reference 25

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This paper cites Edge-labeling Graph Neural Network for Few-shot Learning.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Edge-labeling Graph Neural Network for Few-shot Learning

Reference 26

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This paper cites Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

Reference 27

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions

Reference 28

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Reference 29

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification

Reference 30

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification

Reference 31

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Learning Transferable Visual Models From Natural Language Supervision

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation ImageNet-21K Pretraining for the Masses

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation A Simple Neural Attentive Meta-Learner

Reference 34

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Meta Networks

Reference 35

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This paper cites Laplacian Regularized Few-Shot Learning.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Laplacian Regularized Few-Shot Learning

Reference 36

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Generative Adversarial Networks

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Auto-Encoding Variational Bayes

Reference 38

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Unresolved cited work

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

Reference 40

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation & Salakhutdinov, R

Reference 41

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation & Friedman, J

Reference 42

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation & Courville, A

Reference 43

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Decoupled Weight Decay Regularization

Reference 44

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Bag of Tricks for Image Classification with Convolutional Neural Networks

Reference 45

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Context-Aware Meta-Learning

Reference 46

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

Reference 47

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Transductive Decoupled Variational Inference for Few-Shot Classification

Reference 48

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation & Korman, S

Reference 49

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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation The Balanced-Pairwise-Affinities Feature Transform

Reference 50

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This paper cites Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning

Reference 51

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This paper cites Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification

Reference 52

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This paper cites Task Augmentation by Rotating for Meta-Learning.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Task Augmentation by Rotating for Meta-Learning

Reference 53

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This paper cites Generalized Adaptation for Few-Shot Learning.

ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation Generalized Adaptation for Few-Shot Learning

Reference 54

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