Papers with Code 아카이브
Generalized Few-Shot Classification
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
LIFT+: Lightweight Fine-Tuning for Long-Tail Learning
Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition
Learning from Neighbors: Category Extrapolation for Long-Tail Learning
Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition
AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
Representation Norm Amplification for Out-of-Distribution Detection in Long-Tail Learning
LTRL: Boosting Long-tail Recognition via Reflective Learning
On Characterizing and Mitigating Imbalances in Multi-Instance Partial Label Learning
Adaptive Parametric Activation
Enhanced Long-Tailed Recognition with Contrastive CutMix Augmentation
Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition
Boosting Model Resilience via Implicit Adversarial Data Augmentation
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
Probabilistic Contrastive Learning for Long-Tailed Visual Recognition
SURE: SUrvey REcipes for building reliable and robust deep networks
EAT: Towards Long-Tailed Out-of-Distribution Detection
Long-Tail Learning with Rebalanced Contrastive Loss
Data-Centric Long-Tailed Image Recognition
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning. paper with code
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
Variance-Covariance Regularization Improves Representation Learning
Norm-guided latent space exploration for text-to-image generation
On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning
Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
Feature-Balanced Loss for Long-Tailed Visual Recognition
Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels
Improving Image Recognition by Retrieving from Web-Scale Image-Text Data
Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
Pseudo Labels Regularization for Imbalanced Partial-Label Learning
Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.