HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Long-tail Learning
Long Tail Learning On Cifar 100 Lt R 50
Long Tail Learning On Cifar 100 Lt R 50
Metrics
Error Rate
Results
Performance results of various models on this benchmark
Columns
Model Name
Error Rate
Paper Title
LDAM-DRW + SSP
52.89
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
LDAM-DRW-RSG
51.5
RSG: A Simple but Effective Module for Learning Imbalanced Datasets
Hybrid-PSC
51.07
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
CBD+TailCalibX
49.1
Feature Generation for Long-tail Classification
MetaSAug-LDAM
47.73
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
MiSLAS
47.7
Improving Calibration for Long-Tailed Recognition
GCL
46.4
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
TADE
46.1
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
BCL(ResNet-32)
43.4
Balanced Contrastive Learning for Long-Tailed Visual Recognition
NCL(ResNet32)
43.2
Nested Collaborative Learning for Long-Tailed Visual Recognition
Difficulty-Net
43.1
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition
LTR-weight-balancing
42.29
Long-Tailed Recognition via Weight Balancing
PC
42.25
Learning Prototype Classifiers for Long-Tailed Recognition
GML (ResNet-32)
41.9
Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels
OPeN (WideResNet-28-10)
40.2
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
MDCS
39.9
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
DeiT-LT
39.5
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
SURE(ResNet-32)
36.87
SURE: SUrvey REcipes for building reliable and robust deep networks
GLMC (ResNet-34, channel x4)
36.15
Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions
GLMC + SAM
34.72
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
0 of 25 row(s) selected.
Previous
Next