  We revisit the long-standing problem of learning from a small sample, to
which end we propose a novel method called GLICO (Generative Latent Implicit
Conditional Optimization). GLICO learns a mapping from the training examples to
a latent space and a generator that generates images from vectors in the latent
space. Unlike most recent works, which rely on access to large amounts of
unlabeled data, GLICO does not require access to any additional data other than
the small set of labeled points. In fact, GLICO learns to synthesize completely
new samples for every class using as little as 5 or 10 examples per class, with
as few as 10 such classes without imposing any prior. GLICO is then used to
augment the small training set while training a classifier on the small sample.
To this end, our proposed method samples the learned latent space using
spherical interpolation, and generates new examples using the trained
generator. Empirical results show that the new sampled set is diverse enough,
leading to improvement in image classification in comparison with the state of
the art, when trained on small samples obtained from CIFAR-10, CIFAR-100, and
CUB-200.
