9 citations · 21 across the 9 of their papers we have counts for
10 papers
Towards a Hypothesis on Visual Transformation based Self-Supervision
Dipan K. Pal, Sreena Nallamothu, Marios Savvides
We propose the first qualitative hypothesis characterizing the behavior of visual transformation based self-supervision, called the VTSS hypothesis. Given a dataset upon which a se…
Learning Non-Parametric Invariances from Data with Permanent Random Connectomes
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. Most prior art h…
Proximal Splitting Networks for Image Restoration
Raied Aljadaany, Dipan K. Pal, Marios Savvides
Image restoration problems are typically ill-posed requiring the design of suitable priors. These priors are typically hand-designed and are fully instantiated throughout the proce…
Ring loss: Convex Feature Normalization for Face Recognition
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. We argue that…
Non-Parametric Transformation Networks
Dipan K. Pal, Marios Savvides
ConvNets, through their architecture, only enforce invariance to translation. In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Tran…
Class Correlation affects Single Object Localization using Pre-trained ConvNets
Pokkalla Harsha Vardhan, Kunal Sekhri, Dipan K. Pal +1
The problem of object localization has become one of the mainstream problems of vision. Most of the algorithms proposed involve the design for the model to be specifically for loca…
Max-Margin Invariant Features from Transformed Unlabeled Data
Dipan K. Pal, Ashwin A. Kannan, Gautam Arakalgud +1
The study of representations invariant to common transformations of the data is important to learning. Most techniques have focused on local approximate invariance implemented with…
How ConvNets model Non-linear Transformations
Dipan K. Pal, Marios Savvides
In this paper, we theoretically address three fundamental problems involving deep convolutional networks regarding invariance, depth and hierarchy. We introduce the paradigm of Tra…
Emergence of Selective Invariance in Hierarchical Feed Forward Networks
Dipan K. Pal, Vishnu Boddeti, Marios Savvides
Many theories have emerged which investigate how in- variance is generated in hierarchical networks through sim- ple schemes such as max and mean pooling. The restriction to max/me…
Unitary-Group Invariant Kernels and Features from Transformed Unlabeled Data
Dipan K. Pal, Marios Savvides
The study of representations invariant to common transformations of the data is important to learning. Most techniques have focused on local approximate invariance implemented with…