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Found 11 papers, 0 papers with code
Towards a Hypothesis on Visual Transformation based Self-Supervision
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
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
This is in contrast to the common practice in literature of having the prior to be fixed and fully instantiated even during training stages.
Ring loss: Convex Feature Normalization for Face Recognition
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.
Non-Parametric Transformation Networks
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.
Class Correlation affects Single Object Localization using Pre-trained ConvNets
The problem of object localization has become one of the mainstream problems of vision.
Max-Margin Invariant Features from Transformed Unlabeled Data
The study of representations invariant to common transformations of the data is important to learning.
How ConvNets model Non-linear Transformations
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
We illustrate the general notion of selective invari- ance through object categorization experiments on large- scale datasets such as SVHN and ILSVRC 2012.
Discriminative Invariant Kernel Features: A Bells-and-Whistles-Free Approach to Unsupervised Face Recognition and Pose Estimation
We propose an explicitly discriminative and `simple' approach to generate invariance to nuisance transformations modeled as unitary.
Unitary-Group Invariant Kernels and Features from Transformed Unlabeled Data
The study of representations invariant to common transformations of the data is important to learning.
