Treating abnormal events as a binary classification problem is not ideal for two reasons :

  1. Abnormal events are challenging to obtain due to their rarity.
  2. There is a massive variety of abnormal events, and manually detecting and labeling such events is a difficult task that requires much manpower.

A better approach would be to use unlabelled video sequences with little or no abnormal events to train which are obtained easily . Autoencoders require just that

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The above flowchart explains the working of a trained ConvLSTM Autoencoder

  • Reconstructed clip is output by the Autoencoder
  • Based on the error (reconstruction cost) between the original and reconstructed clip we can find if a particular frame is…

The entire paper can be summarized in three stages :

Self-supervised learning → Clustering → Self labelling

Self supervised learning : (Mining K nearest neighbors)

A typical image classification task would involve labels to govern the features it learns through a Loss function . But when there are no labels to govern such backpropagation in a network how do we get the network to learn meaningful features from the images ?

Self — supervised representation learning involves the use of a predefined task/objective to make sure the network learns meaningful features . Let’s take a NN of 5 layers , once we have a good representation of the image (an xD vector of the 5th layer) , we can cluster them using Euclidean distance as a loss function to cluster the images . But we have no idea if this will be semantically meaningful and moreover this approach will tend to focus on low level features during backprop and hence is dependent on the initialization used in the first…

A story using GRE words (Group 2 : 30 words)

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These groups are part of GregMat’s vocab list . You can check those out in the links below

Children today are adulterated with unimportant tasks which they perceive as important . Experts advocate a change in parents behavior to automatically nudge them to decide which are actually important. Privileged kids are burdened with the responsibility of aggrandizing their parents’ reputation which eventually makes them lose the alacrity to do what they love . What happens in turn is their thoughts risk the nature of being ambivalent all the time . Soon it might become hard to ameliorate their decision making skills . …

A story using GRE words (Group 1 : 30 words)

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These groups are part of GregMat’s vocab list . You can check those out in the links below

Here we go !

Thoughts abound when in solitude , thanks to COVID-19, some endearing some not so. A state which we considered order turned amorphous if not chaos . We have been forced into or gifted an austere life . A lot of questions being raised with belied responses . Each day with a capricious outcome, not many can handle . funny how a fear of a predictable outcome can result in such a capricious one . A lot of numbers being followed , caught in a dilemma of being cerebral or empathetic . All hail the souls who are far away from home with no congenial company . …

Correlation is the first step in finding relationships between quantities and deserves some attention . Correlation is defined as the association between quantities , for eg, the sales might increase when income of people increases

Before we dwell into the math , we need to understand Co-variance . Co-variance is the statistical measure of association between variables

Cov(x,y) = E [ (x — E[x]) (y — E[y]) ]

The equation above is equation for Co-variance , let’s break this down

E denotes the expected value of a variable , which is nothing but the mean . x — E[x] is nothing but the x values subtracted from its mean which eventually gives the deviations from the mean. We do the same for y and we multiply deviations of x and y from their respective mean . …


Shyam Sundar

Project Engineer at e-con systems | Deep learning (Computer vision) | Great lakes PGP DSE

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