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Found 10 papers, 2 papers with code
Hyperplane Arrangements of Trained ConvNets Are Biased
We investigate the geometric properties of the functions learned by trained ConvNets in the preactivation space of their convolutional layers, by performing an empirical study of hyperplane arrangements induced by a convolutional layer.
Geometry of Deep Convolutional Networks
We give a formal procedure for computing preimages of convolutional network outputs using the dual basis defined from the set of hyperplanes associated with the layers of the network.
Spotlight the Negatives: A Generalized Discriminative Latent Model
Discriminative latent variable models (LVM) are frequently applied to various visual recognition tasks.
Visual Instance Retrieval with Deep Convolutional Networks
This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval.
Persistent Evidence of Local Image Properties in Generic ConvNets
Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or the variation within the object class.
Factors of Transferability for a Generic ConvNet Representation
In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target).
Large Scale, Large Margin Classification using Indefinite Similarity Measures
Despite the success of the popular kernelized support vector machines, they have two major limitations: they are restricted to Positive Semi-Definite (PSD) kernels, and their training complexity scales at least quadratically with the size of the data.
Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach
Finally, we show that state of the art object detection methods (e. g. DPM) are unable to use the tails of this distribution comprising 50\% of the training samples.
CNN Features off-the-shelf: an Astounding Baseline for Recognition
We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the \overfeat network which was trained to perform object classification on ILSVRC13.
3D Pictorial Structures for Multiple View Articulated Pose Estimation
We consider the problem of automatically estimating the 3D pose of humans from images, taken from multiple calibrated views.
