• Corpus ID: 235458538

Gone Fishing: Neural Active Learning with Fisher Embeddings

@inproceedings{Ash2021GoneFN,
  title={Gone Fishing: Neural Active Learning with Fisher Embeddings},
  author={Jordan T. Ash and Surbhi Goel and Akshay Krishnamurthy and Sham M. Kakade},
  booktitle={NeurIPS},
  year={2021}
}
There is an increasing need for effective active learning algorithms that are compatible with deep neural networks. This paper motivates and revisits a classic, Fisher-based active selection objective, and proposes BAIT, a practical, tractable, and high-performing algorithm that makes it viable for use with neural models. BAIT draws inspiration from the theoretical analysis of maximum likelihood estimators (MLE) for parametric models. It selects batches of samples by optimizing a bound on the… 

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References

SHOWING 1-10 OF 56 REFERENCES

Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds

TLDR
This work designs a new algorithm for batch active learning with deep neural network models that samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, and shows that while other approaches sometimes succeed for particular batch sizes or architectures, BADGE consistently performs as well or better, making it a versatile option for practical active learning problems.

Deep Active Learning over the Long Tail

TLDR
A novel active learning algorithm that queries consecutive points from the pool using farthest-first traversals in the space of neural activation over a representation layer shows consistent and overwhelming improvement in sample complexity over passive learning (random sampling) for three datasets: MNIST, CIFar-10, and CIFAR-100.

Active Learning for Convolutional Neural Networks: A Core-Set Approach

TLDR
This work defines the problem of active learning as core-set selection as choosing set of points such that a model learned over the selected subset is competitive for the remaining data points, and presents a theoretical result characterizing the performance of any selected subset using the geometry of the datapoints.

Adversarial Active Learning for Deep Networks: a Margin Based Approach

TLDR
It is demonstrated empirically that adversarial active queries yield faster convergence of CNNs trained on MNIST, the Shoe-Bag and the Quick-Draw datasets.

Agnostic active learning

TLDR
The first active learning algorithm which works in the presence of arbitrary forms of noise is state and analyzed, and it is shown that A2 achieves an exponential improvement over the usual sample complexity of supervised learning.

On Warm-Starting Neural Network Training

TLDR
A closer look is taken at this empirical phenomenon, warm-starting neural network training, which seems to yield poorer generalization performance than models that have fresh random initializations, even though the final training losses are similar.

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning

We develop BatchBALD, a tractable approximation to the mutual information between a batch of points and model parameters, which we use as an acquisition function to select multiple informative points

Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning

TLDR
This paper shows that FIR can be asymptotically viewed as an upper bound of the expected variance of the log-likelihood ratio, and suggests a unifying framework that not only enables to make theoretical comparisons among the existing querying methods based on FIR, but also allows to give insight into the development of new active learning approaches.

Active Deep Learning with Fisher Information for Patch-Wise Semantic Segmentation

TLDR
A novel diversified AL based on Fisher information (FI) for the first time for CNNs, where gradient computations from backpropagation are used for efficient computation of FI on the large CNN parameter space to achieve accuracy higher than entropy-based querying in transfer learning.

Deep Bayesian Active Learning with Image Data

TLDR
This paper develops an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature, and demonstrates its active learning techniques with image data, obtaining a significant improvement on existing active learning approaches.
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