Why I chose industry over academy

In general, if I need to choose something over some other thing I enlist the positive and negative facts about options and have a basic summation to find out the correct one.

Here I itemize my subjective pros and cons list. Maybe you might find it skewed or ridiculous but these are based on my 3 years of hard core academic effort and 2 years in industry (sum of my partial efforts). I think they present at least some of the obstacles you would see in the both worlds.

 

First, I start with the academy;

Pros--

  1. Academic life is the best in terms of freedom at work. You choose your study topic, at least to some extent, you team-up and follow the boundaries of human knowledge so as to extend it a bit. This is a very respectful and curious search. For sure, it is better than having a boss choosing your way to go. However , even this freedom is limited as in  the below comic :)
  2. Dresscode. Yes, academy is not so certain to define a particular dresscode for you, in most cases. You are free to put on your comfortable shorts and flip-flops and go to your office to work. However, it should be pointed out that present industry also realized the idiocy of strict dresscodes and it provides better conditions for the employees as well. Yet, business is still not comparable with the academy.2345
  3. Travel around the world with conferences, summer-schools, meetings, internships at low-cost. Meet people around the globe and feel the international sense.
  4. Respectful job. It urges the sense of respect as you say you are an academic and  people usually assume you are more intelligent than the most, thanks to great scientist ancestors.
  5. Set your schedule. Schedule of an academic is more flexible and you have a bit of freedom to define your work time.
  6. Teaching. It is really great to envision young people with your knowledge and experience. Even-more, it is a vital role in a society since you are able to shape the future with the young people you touch.
  7. Elegant social circle. Being an academic chains you with a social circle of people with a similar education level and supposedly similar level of cultivation. That of course does not mean that the industry consists of the ignorant but living in corporate life is more susceptible to facing unfortunate minds.

Read On…

I presented my Master Dissertation?

I am glad to be presented my master dissertation at the end, I collect valuable feedback from my community. Before, I shared what I have done so far for the thesis on the different posts. CMAP (Concept Map) and FAME (Face Association through Model Evolution) (I called it AME at the Thesis for being more generic) are basically two different method for mining visual concepts from noisy image sources such as Google Image Search or Flickr. You might prefer to look at the posts for details or I posted here also the presentation for the brief view of my work.

 

 

FAME: Face Association through Model Evolution

Here, I summarize a new method called FAME for learning Face Models from noisy set of web images. I am studying this for my MS Thesis. To be a little intro to my thesis, the title is "Mining Web Images for Concept Learning" and it introduces two new methods for automatic learning of visual concepts from noisy web images. First proposed method is FAME and the other work was presented here before, that is namely ConceptMap and it is accepted for ECCV14 (self promotion :)).

Before I start, I should disclaim that FAME is not a fully furnished work and waiting your valuable comments. Please leave your statements about anything you find useful, ridiculous, awkward or great.

In this work, we grasp the problem of learning face models for public faces from images collected from web through querying a particular person name. Collected images are called weakly-labelled by the rough prescription of defined query. However, the data is very noisy even after face detection, with false detections or several irrelevant faces Read On…

Our ECCV2014 work "ConceptMap: Mining noisy web data for concept learning"

---- I am living the joy of seeing my paper title on the list of accepted ECCV14 papers :). Seeing the outcome of your work makes worthwhile all your day to night efforts, REALLY!!!. Before start, I shall thank to my supervisor Pinar Duygulu for her great guidance.----

In this post, I would like to summarize the title work since I believe sometimes a friendly blog post might be more expressive than a solid scientific article.

"ConceptMap: Mining noisy web data for concept learning" proposes a pipeline so as to learn wide range of visual concepts by only defining a query to a image search engine. The idea is to query a concept at the service and download a huge bunch of images. Cluster images as removing the irrelevant instances. Learn a model from each of the clusters. At the end, each concept is represented by the ensemble of these classifiers. Read On…

Large data really helps for Object Detection ?

I stumbled upon a interesting BMVC 2012 paper (Do We Need More Training Data or Better Models for Object Detection? -- Zhu, Xiangxin, Vondrick, Carl, Ramanan, Deva, Fowlkes, Charless). It is claming something contrary to current notion of big data theory that advocates benefit of large data-sets so as to learn better models with increasing training data size. Nevertheless, the paper states that large training data is not that much helpful for learning better models, indeed more data is maleficent without careful tuning of your system !! Read On…

How does Feature Extraction work on Images?

Here I share enhanced version of one of my Quora answer to a similar question ...

There is no single answer for this question since there are many diverse set of methods to extract feature from an image.

First, what is called feature? "a distinctive attribute or aspect of something." so the thing is to have some set of values for a particular instance that diverse that instance from the counterparts. In the field of images, features might be raw pixels for simple problems like digit recognition of well-known Mnist dataset. However, in natural images, usage of simple image pixels are not descriptive enough. Instead there are two main steam to follow. One is to use hand engineered feature extraction methods (e.g. SIFT, VLAD, HOG, GIST, LBP) and the another stream is to learn features that are discriminative in the given context (i.e. Sparse Coding, Auto Encoders, Restricted Boltzmann Machines, PCA, ICA, K-means). Note that second alternative, Read On…

A Large set of Machine Learning Resources for Beginners to Mavens

Best way to qualify your machine learning model.

Selection of your final machine learning model is a vital part of your project. Using the accurate metric and the selection paradigm might give very good results even you use very simple or even wrong learning algorithm. Here, I explain a very parsimonious and plane way.

The metric you choose is depended to your problem end expectations. Some common alternatives are F1 score (combination of precision and recall), accuracy (ratio of correctly classified instances to all instances), ROC curve or error rate (1-accuracy).

For being an example I use error rate (at the below figure). First divide the data into 3 as train set, held-out set, test set. We will use held-out set as an objective guidance of hyper-parameters of your algorithm. You might also prefer to use K-fold X-validation but my choice is to keep a held-out set, if I have enough number of instances.

Following procedure can be used for parameter selection and the selection of the final model. The idea is, plotting the performance of the model with the lines of test fold accuracy (held-out set) and the train fold accuracy. This plot should be met at a certain point where both of the curves consistent in some sense (training fold and test fold scores are at reasonable levels) and after a slight step they start to be stray away from each other (train fold score increases still and test fold score starts to be dropped down). This straying effect might be underfitting or after a numerous learning iterations likely to be overfitting.  Choice the best trade-off point on the plot as the correct model.

 

Example with error rate so not confused by the decreasing values so lower is better in that sense. The signed point is the saturation point where the data starts to over-fit.


Another caveat, do not use so much folds for x-validation since some of the papers (that cannot come up the name right now:( ), asymptotic behaviour of cross validation is likely to tout over-fitting therefore use of leave-multiple out procedure instead of leave-one out if you propose to use large fold number.