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Models/ETF Classifier + DR (Resnet)

ETF Classifier + DR (Resnet)

Reported on 10 benchmarks across 5 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology6 results

  • Generalized Few-Shot ClassificationonCIFAR-100-LT (ρ=100)
    Error Rate· 2022-03-17
    54.7
    best: 10.9 (LPT)
    Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?arXiv:2203.09081
  • Generalized Few-Shot ClassificationonCIFAR-10-LT (ρ=100)
    Error Rate· 2022-03-17
    23.5
    best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
    Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?arXiv:2203.09081
  • Long-tail LearningonCIFAR-100-LT (ρ=100)
    Error Rate· 2022-03-17
    54.7
    best: 10.9 (LPT)
    Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?arXiv:2203.09081
  • Long-tail LearningonCIFAR-10-LT (ρ=100)
    Error Rate· 2022-03-17
    23.5
    best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
    Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?arXiv:2203.09081
  • Generalized Few-Shot LearningonCIFAR-100-LT (ρ=100)
    Error Rate· 2022-03-17
    54.7
    best: 10.9 (LPT)
    Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?arXiv:2203.09081
  • Generalized Few-Shot LearningonCIFAR-10-LT (ρ=100)
    Error Rate· 2022-03-17
    23.5
    best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
    Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?arXiv:2203.09081

Computer Vision4 results

  • Image ClassificationonCIFAR-100-LT (ρ=100)
    Error Rate· 2022-03-17
    54.7
    best: 10.9 (LPT)
    Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?arXiv:2203.09081
  • Image ClassificationonCIFAR-10-LT (ρ=100)
    Error Rate· 2022-03-17
    23.5
    best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
    Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?arXiv:2203.09081
  • Few-Shot Image ClassificationonCIFAR-100-LT (ρ=100)
    Error Rate· 2022-03-17
    54.7
    best: 10.9 (LPT)
    Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?arXiv:2203.09081
  • Few-Shot Image ClassificationonCIFAR-10-LT (ρ=100)
    Error Rate· 2022-03-17
    23.5
    best: 10.42 (GLMC+MaxNorm (ResNet-34, channel x4))
    Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?arXiv:2203.09081