Search Results for author: Dipan K. Pal

Found 11 papers, 0 papers with code

Towards a Hypothesis on Visual Transformation based Self-Supervision

no code implementations24 Nov 2019 Dipan K. Pal, Sreena Nallamothu, Marios Savvides

Overall, this paper aims to shed light on the phenomenon of visual transformation based self-supervision.


Learning Non-Parametric Invariances from Data with Permanent Random Connectomes

no code implementations13 Nov 2019 Dipan K. Pal, Akshay Chawla, Marios Savvides

One of the fundamental problems in supervised classification and in machine learning in general, is the modelling of non-parametric invariances that exist in data.

Proximal Splitting Networks for Image Restoration

no code implementations17 Mar 2019 Raied Aljadaany, Dipan K. Pal, Marios Savvides

This is in contrast to the common practice in literature of having the prior to be fixed and fully instantiated even during training stages.

Image Denoising Image Restoration +1

Ring loss: Convex Feature Normalization for Face Recognition

no code implementations CVPR 2018 Yutong Zheng, Dipan K. Pal, Marios Savvides

We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax.

Face Identification Face Recognition +1

Non-Parametric Transformation Networks

no code implementations14 Jan 2018 Dipan K. Pal, Marios Savvides

In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn \textit{general} invariances and symmetries directly from data.


How ConvNets model Non-linear Transformations

no code implementations24 Feb 2017 Dipan K. Pal, Marios Savvides

In this paper, we theoretically address three fundamental problems involving deep convolutional networks regarding invariance, depth and hierarchy.

Emergence of Selective Invariance in Hierarchical Feed Forward Networks

no code implementations30 Jan 2017 Dipan K. Pal, Vishnu Boddeti, Marios Savvides

We illustrate the general notion of selective invari- ance through object categorization experiments on large- scale datasets such as SVHN and ILSVRC 2012.

Object Categorization

Unitary-Group Invariant Kernels and Features from Transformed Unlabeled Data

no code implementations18 Nov 2015 Dipan K. Pal, Marios Savvides

The study of representations invariant to common transformations of the data is important to learning.

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