Zero Shot Learning

Zero Shot Learning

My Summary 

• Extract Attributes from Data (Images - Edges, Corners, Contours, Fetaure vectors)
• Textual embedding or feature vector
• Using this Classify known or unknown Class

Zero-Shot Learning - Feature / Attribute extraction and prediction based on those features of known class and heard features of unknown class

Feedback - Good concept, For all these cases we need reasonable data to extract, build features, and discriminative features.

Some conceptual notes/papers

• CNN learning algorithm to learn to detect the features of the word-embeddings like stripes, animalness, and whiteness in images as well.
• Replace the label of the image with its word-embedding during training.
• Pre-trained word-embeddings can be downloaded and used with the object recognition CNN model.

ZSL

• Zero-shot methods basically work by combining the observed/seen and non-observed/unseen categories
• There are two common approaches used to solve the zero-shot recognition problems.
• Embedding based approach
• Generative model-based approach
• Zero-shot classification model is trained on both seen and non-observed category images at train time

From classification - Set of X, Not belongs to X, Belongs to set ox X class vs Not belongs to X set

Zero Shot Learning 

• Zero-shot classification refers to the problem setting where we want to recognize objects from classes that our model has not seen during training
• Seen classes: These are classes for which we have labelled images during training
• Unseen classes: These are classes for which labelled images are not present during the training phase.
• Auxiliary information: This information consists of descriptions/semantic attributes/word embeddings

If I had to sum up ZSL in a few words, I’d say that it is:

• Pattern recognition without training examples
• Based on semantic transfer

Representation Learning

Zero-shot learning approach

• Training is the process of capturing knowledge about the qualities.
• Inference where the information is utilized to classify examples into a new set of classes.

Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective

• Classifying visual features based on the classifiers learned from the semantic descriptions
• Highly discriminative classifiers for seen classes and the generated classifiers for unseen classes to classify visual features of all classes

Zero-shot Learning with Deep Neural Networks for Object Recognition∗

• Zero-Shot Learning with Semantic Output Codes
• Zero-shot Learning with Deep Neural Networks for Object Recognition∗

Keep Exploring!!!