Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature Generation for Long-tail Classification   The visual world naturally exhibits an imbalance in the number of object or
scene instances resulting in a \emph{long-tailed distribution}. This imbalance
poses significant challenges for classification models based on deep learning.
Oversampling instances of the tail classes attempts to solve this imbalance.
However, the limited visual diversity results in a network with poor
representation ability. A simple counter to this is decoupling the
representation and classifier networks and using oversampling only to train the
classifier. In this paper, instead of repeatedly re-sampling the same image
(and thereby features), we explore a direction that attempts to generate
meaningful features by estimating the tail category's distribution. Inspired by
ideas from recent work on few-shot learning, we create calibrated distributions
to sample additional features that are subsequently used to train the
classifier. Through several experiments on the CIFAR-100-LT (long-tail) dataset
with varying imbalance factors and on mini-ImageNet-LT (long-tail), we show the
efficacy of our approach and establish a new state-of-the-art. We also present
a qualitative analysis of generated features using t-SNE visualizations and
analyze the nearest neighbors used to calibrate the tail class distributions.
Our code is available at https://github.com/rahulvigneswaran/TailCalibX.

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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: eess
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The categories of this paper are: physics
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The categories of this paper are: econ
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The categories of this paper are: solv-int