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

Generative Latent Implicit Conditional Optimization when Learning from Small Sample