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Zero shot learning

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Kishor Datta Gupta
Kishor Datta Gupta

Zero Shot Learning using word2vec and attribute based image classification

Engineering
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Zero Shot Learning
ncat.edu ProgressReport 2 What is 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 for both seen and unseen classes at train time. This information acts as a bridge between seen and unseen classes
ncat.edu ProgressReport 3 Example: . Suppose, We have a classification problem with class label : Dog, Cat, Mouse, Car, Bus, Train Seen Class: Dog, Fox, Bus, Train UnSeen Class: Wolf, Car Auxiliary Information: word2vec of all 6 class
ncat.edu 4 Suppose, word2vec in 2d (x,y) Dog : (2,10) Wolf : (4,8) Fox : (2,7) Car : (6,4) Bus : (6,3) Train : (7,3) Word2Vec as Auxiliary Information
ncat.edu 5 Dog Fox Bus Train 2,10 Dog Fox Bus Train 2,7 6,3 7,3 Training the Neural Net without unseen Class Neural Network try to train the image to predict the word2vector value, closest one is consider the predicted class Traditional training Zero Shot training
ncat.edu 6 Dog Wolf Fox Bus Train Car Testing the Neural Net without unseen Class Output vector value closest word2vec value from seen and unseen class measure using Euclidian distance Word2vec Value V
ncat.edu ProgressReport 7 Example 2: . Suppose, We have a Alphabet classification problem with English Alphabet: Seen Class: A,B,C,E,F,G,I,J,K,L,M,N,O,P,Q,S,T,U,W,X,Y UnSeen Class: “ J”, “D”, “H”, “R”, “Z” Auxiliary Information: Attributes
ncat.edu 8 10 out of 15 manually designed features Auxiliary Information
ncat.edu 9 Auxiliary Information In the dataset class label was replaced With the category vectors
ncat.edu 10 Training No Softmax Model is trying to predict 15 features
ncat.edu 11 Testing Input Euclidian Distance between all alphabet and the output Closest one is the class label

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