Learning from Limited and Imperfect Data

The datasets used for Deep Neural Network training (e.g., ImageNet, MSCOCO, etc.) are often manually balanced across categories (classes) to facilitate learning of all the categories. This curation process is often expensive and requires throwing away precious annotated data to balance the frequency across classes. This is because the distribution of data in the world (e.g., internet, etc.) significantly differs from the well-curated datasets and is often over-populated with samples from common categories. The algorithms designed for well-curated datasets perform suboptimally when used to learn from imperfect datasets with long-tailed imbalances and distribution shifts. For deep models to be widely used, getting away with the costly curation process by developing robust algorithms that can learn from real-world data distribution is necessary. Toward this goal, we develop practical algorithms for Deep Neural Networks that can learn from limited and imperfect data present in the real world. These works are divided into four segments, each covering a scenario of learning from limited or imperfect data. The first part of the works focuses on Learning Generative Models for Long-Tail Data, where we mitigate the mode-collapse for tail (minority) classes and enable diverse aesthetic image generations as head (majority) classes. In the second part, we enable effective generalization on tail classes through Inductive Regularization schemes, which allow tail classes to generalize as the head classes without enforcing explicit generation of images. In the third part, we develop algorithms for Optimizing Relevant Metrics compared to the average accuracy for learning from long-tailed data with limited annotation (semi-supervised), followed by the fourth part, which focuses on the effective domain adaptation of the model to various domains with zero to very few labeled samples.