STAT946F17/Cognitive Psychology For Deep Neural Networks: A Shape Bias Case Study

From statwiki
Revision as of 14:59, 18 October 2017 by Vrrao (talk | contribs) (Created page with "= Introduction = The recent burgeon on the use of Deep Neural Networks (DNNs) have resulted in giant leaps of accuracy in prediction. They are also being used to solve a vari...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

Introduction

The recent burgeon on the use of Deep Neural Networks (DNNs) have resulted in giant leaps of accuracy in prediction. They are also being used to solve a variety of complex tasks which earlier methodologies have struggled to excel in.

While it is all good to see incredibly high accuracy as a result of the use of DNN, we must begin to question why they perform so well. It has become an interesting field of study to actually represent the features/feature maps or interpret the meaning of the learnt values in a DNN's hidden layers. Currently we treat models of DNNs as black boxes which we practically tune the tweakable parameters like number of layers, number of units in each layer, number & size of feature maps(in case of CNN) etc. The opacity created by the lack of an intuitive representation of the internal learnt parameters of DNNs hinders both basic research as well as its application to real world problems.

Recent pushes have aimed to better understand DNNs: tailor-made loss functions and architectures produce more interpretable features (Higgins et al., 2016; Raposo et al., 2017) while output-behavior analyses unveil previously opaque operations of these networks (Karpathy et al., 2015). Parallel to this work, neuroscience-inspired methods such as activation visualization (Li et al., 2015), ablation analysis (Zeiler & Fergus, 2014) and activation maximization (Yosinski et al., 2015) have also been applied

This paper aims to provide another methodology to attempt to decipher & better understand how DNNs solve a particular task. This methodology was inspired by psychological concepts to test whether the DNN's were able to make accurate predictions with biases similar to that the human mind makes.

Research in developmental psychology shows that when learning new words, humans tend to assign the same name to similarly shaped items rather than to items with similar color, texture, or size. This bias/knowledge tend to be forged into the brains of humans and humans then take this forward to easily associate these shapes with new objects they have not seen before.

The authors of this paper try to simulate if DNNs behave similarly in one-shot learning applications. They attempt to prove that when the models of state-of-the-art DNNs are used to learn objects from images, they exhibit a stronger shape bias than a color bias. To emulate the human brain, they use the parameters of pre-trained DNN models and use this to perform one-shot learning on a new data set with different labels.

Background

One Shot Learning

One-shot learning is an object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images.

The one-shot word learning task is to label a novel data example $\hat{x}$ (e.g. a novel probe image) with a novel class label $\hat{y}$ (e.g. a new word) after only a single example.

More specifically, given a support set $S = {(x_i, y_i) , i \in [1, k]}$, of images $x_i$, and their associated labels $y_i$, and an unlabeled probe image $\hat{x}$, the one-shot learning task is to identify the true label of the probe image, $\hat{y}$, from the support set labels $ {y_i , i \in [1, k]} $:


$\displaystyle \hat{y} = arg \max_{y}$ $P(y | \hat{x}, S)$


We assume that the image labels $y_i$ are represented using a one-hot encoding and that $P(y|\hat{x}, S)$ is parameterised by a DNN, allowing us to leverage the ability of deep networks to learn powerful representations.

Inception Networks

Matching Networks

Methodology

Evaluation

Datasets

Experiment 1:

Experiment 2:

Future Work and Open questions

References