📒
PaperNotes
  • PAPER NOTES
  • Meta-Learning with Implicit Gradient
  • DARTS: Differentiable Architecture Search
  • Meta-Learning of Neural Architectures for Few-Shot Learning
  • Towards Fast Adaptation of Neural Architectures with Meta Learning
  • Editable Neural Networks
  • ANIL (Almost No Inner Loop)
  • Meta-Learning Representation for Continual Learning
  • Learning to learn by gradient descent by gradient descent
  • Modular Meta-Learning with Shrinkage
  • NADS: Neural Architecture Distribution Search for Uncertainty Awareness
  • Modular Meta Learning
  • Sep
    • Incremental Few Shot Learning with Attention Attractor Network
    • Learning Steady-States of Iterative Algorithms over Graphs
      • Experiments
    • Learning combinatorial optimization algorithms over graphs
    • Meta-Learning with Shared Amortized Variational Inference
    • Concept Learners for Generalizable Few-Shot Learning
    • Progressive Graph Learning for Open-Set Domain Adaptation
    • Probabilistic Neural Architecture Search
    • Large-Scale Long-Tailed Recognition in an Open World
    • Learning to stop while learning to predict
    • Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
    • Learning to Generalize: Meta-Learning for Domain Generalization
  • Oct
    • Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
    • Network Architecture Search for Domain Adaptation
    • Continuous Meta Learning without tasks
    • Learning Causal Models Online
    • Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
    • Conditional Neural Progress (CNPs)
    • Reviving and Improving Recurrent Back-Propagation
    • Meta-Q-Learning
    • Learning Self-train for semi-supervised few shot classification
    • Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
  • Nov
    • Neural Process
    • Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
    • Learning to Adapt to Evolving Domains
  • Tutorials
    • Relax constraints to continuous
    • MAML, FO-MAML, Reptile
    • Gradient Descent
      • Steepest Gradient Descent
      • Conjugate Gradient Descent
  • KL, Entropy, MLE, ELBO
  • Coding tricks
    • Python
    • Pytorch
  • ml
    • kmeans
Powered by GitBook
On this page
  • Motivation and advantages of the model
  • OLTR (dynamic meta-embedding)
  • Reference:

Was this helpful?

  1. Sep

Large-Scale Long-Tailed Recognition in an Open World

9-25-2020

PreviousProbabilistic Neural Architecture SearchNextLearning to stop while learning to predict

Last updated 4 years ago

Was this helpful?

Motivation and advantages of the model

Real world data often have a long-tailed and open-ended distribution.

Three common problems and their limitations:

  • Imbalanced classification: not sensitive to novelty

  • Few shot learning: cannot avoid forgetting

  • Open set recognition (OOD detection): cannot transfer knowledge

A practical recognition system must classify among majority and minority classes, generalize from a few known instances, and acknowledge novelty upon a never seen instance.

This paper defines Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.

OLTR must handle imbalanced classification, few-shot learning, and open-set recognition in one integrated algorithm, whereas existing classification approaches focus only on one aspect and deliver poorly over the entire class spectrum.

OLTR (dynamic meta-embedding)

The key challenges are how to share visual knowledge between head and tail classes and how to reduce confusion between tail and open classes.

OLTR could realize knowledge transfer, sensitivity to novelty, and avoid forgetting in a unified form.

We develop an integrated OLTR algorithm that maps an image to a feature space such that visual concepts can easily relate to each other based on a learned metric that respects the closed-world classification while acknowledging the novelty of the open world. Our so-called dynamic meta-embedding combines a direct image feature and an associated memory feature, with the feature norm indicating the familiarity to known classes.

Firstly, a visual memory is obtained by aggregating the knowledge from both head and tail classes.

Secondly, the visual concepts stored in the memory are infused back as associated memory feature to enhance the original direct feature. It can be understood as using induced knowledge (i.e. memory feature) to assist the direct observation (i.e. direct feature).

We further learn a concept selector(e in the bottom figure) to control the amount and type of memory feature to be infused. Since head classes already have an abundant direct observation, only a small amount of memory feature is infused for them. On the contrary, tail classes suffer from scarce observation, the associated visual concepts in memory feature are extremely beneficial.

Finally, we calibrate the confidence of open classes by calculating their reachability(No.3 in the bottom figure) to the obtained visual memory

Reference:

https://arxiv.org/pdf/1904.05160.pdf
https://liuziwei7.github.io/papers/longtail_slides.pdf
https://www.youtube.com/watch?v=A45wrs1g8VA
https://liuziwei7.github.io/projects/LongTail.html