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Semi-Supervised Image Classification
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Semi-supervised image classification leverages unlabelled data as well as labelled data to increase classification performance.
You may want to read some blog posts to get an overview before reading the papers and checking the leaderboards:
- An overview of proxy-label approaches for semi-supervised learning - Sebastian Ruder
- Semi-Supervised Learning in Computer Vision - Amit Chaudhary
<span style="color:grey; opacity: 0.6">( Image credit: Self-Supervised Semi-Supervised Learning )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning
Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning
Simple Semi-supervised Knowledge Distillation from Vision-Language Models via $\mathbf{\texttt{D}}$ual-$\mathbf{\texttt{H}}$ead $\mathbf{\texttt{O}}$ptimization
Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision
OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning
SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual Representations
Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification
Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference
A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning
InfoMatch: Entropy Neural Estimation for Semi-Supervised Image Classification
Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning
Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model
Open-World Semi-Supervised Learning for Node Classification
Robust Semi-Supervised Learning for Self-learning Open-World Classes
Color-$S^{4}L$: Self-supervised Semi-supervised Learning with Image Colorization
Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning
Meta Co-Training: Two Views are Better than One
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning
SemiReward: A General Reward Model for Semi-supervised Learning
Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning
Towards Semi-supervised Learning with Non-random Missing Labels
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning
SimMatchV2: Semi-Supervised Learning with Graph Consistency
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data
Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning
RelationMatch: Matching In-batch Relationships for Semi-supervised Learning
Towards Unbiased Training in Federated Open-world Semi-supervised Learning
Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning
Learning Customized Visual Models with Retrieval-Augmented Knowledge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.