Visual Learning and Recognition: Difference between revisions

Visual Learning and Recognition (view source)

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 ==Extra Topics==
 ===Fine-grained Recognition===
+===Few-shot Recognition===
+* Metric learning methods
+* Meta-learning methods
+* Data Augmentation Methods
+* Semantics
+===Zero-shot Recognition===
+Goal is train a classifier without having seen a single labeled example.
+The information comes from a knowledge graph e.g. from word embeddings.
+===Beyond Labelled Datasets===
+* Semi-supervised: We have both labelled and unlabeled training samples.
+* Weakly-supervised: The labels are weak, noisy, and non-necessarily for the task we want.
+* Learning from the Web: Download data from the internet
 ==Will be on the exam==