Towards Complex Features: Competitive Receptive Fields in Unsupervised Deep Networks

2018 ◽  
pp. 838-848 ◽  
Author(s):  
Richard Hankins ◽  
Yao Peng ◽  
Hujun Yin
Keyword(s):  
Receptive Fields ◽  
Deep Networks ◽  
Complex Features

scholarly journals Predicting Propositional Satisfiability via End-to-End Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 3324-3331
Author(s):  
Chris Cameron ◽  
Rex Chen ◽  
Jason Hartford ◽  
Kevin Leyton-Brown
Keyword(s):  
Linear Time ◽  
Network Models ◽  
Problem Size ◽  
Linear Programming Relaxations ◽  
Deep Networks ◽  
Solution Methods ◽  
End To End ◽  
Complex Features ◽  
First Time ◽  
Random Guessing

Strangely enough, it is possible to use machine learning models to predict the satisfiability status of hard SAT problems with accuracy considerably higher than random guessing. Existing methods have relied on extensive, manual feature engineering and computationally complex features (e.g., based on linear programming relaxations). We show for the first time that even better performance can be achieved by end-to-end learning methods — i.e., models that map directly from raw problem inputs to predictions and take only linear time to evaluate. Our work leverages deep network models which capture a key invariance exhibited by SAT problems: satisfiability status is unaffected by reordering variables and clauses. We showed that end-to-end learning with deep networks can outperform previous work on random 3-SAT problems at the solubility phase transition, where: (1) exactly 50% of problems are satisfiable; and (2) empirical runtimes of known solution methods scale exponentially with problem size (e.g., we achieved 84% prediction accuracy on 600-variable problems, which take hours to solve with state-of-the-art methods). We also showed that deep networks can generalize across problem sizes (e.g., a network trained only on 100-variable problems, which typically take about 10 ms to solve, achieved 81% accuracy on 600-variable problems).

scholarly journals Cascaded Deep Networks With Multiple Receptive Fields for Infrared Image Super-Resolution

2019 ◽  
Vol 29 (8) ◽  
pp. 2310-2322 ◽  
Author(s):  
Zewei He ◽  
Siliang Tang ◽  
Jiangxin Yang ◽  
Yanlong Cao ◽  
Michael Ying Yang ◽  
...  
Keyword(s):  
Receptive Fields ◽  
Infrared Image ◽  
Super Resolution ◽  
Deep Networks ◽  
Image Super Resolution

scholarly journals GhoMR: Multi-Receptive Lightweight Residual Modules for Hyperspectral Classification

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6823
Author(s):  
Arijit Das ◽  
Indrajit Saha ◽  
Rafał Scherer
Keyword(s):  
State Of The Art ◽  
Receptive Fields ◽  
Classification Performance ◽  
Data Sets ◽  
Spectral Bands ◽  
Average Accuracy ◽  
Complex Architectures ◽  
Complex Features ◽  
Hyperspectral Classification ◽  
Simple Network

In recent years, hyperspectral images (HSIs) have attained considerable attention in computer vision (CV) due to their wide utility in remote sensing. Unlike images with three or lesser channels, HSIs have a large number of spectral bands. Recent works demonstrate the use of modern deep learning based CV techniques like convolutional neural networks (CNNs) for analyzing HSI. CNNs have receptive fields (RFs) fueled by learnable weights, which are trained to extract useful features from images. In this work, a novel multi-receptive CNN module called GhoMR is proposed for HSI classification. GhoMR utilizes blocks containing several RFs, extracting features in a residual fashion. Each RF extracts features which are used by other RFs to extract more complex features in a hierarchical manner. However, the higher the number of RFs, the greater the associated weights, thus heavier is the network. Most complex architectures suffer from this shortcoming. To tackle this, the recently found Ghost module is used as the basic building unit. Ghost modules address the feature redundancy in CNNs by extracting only limited features and performing cheap transformations on them, thus reducing the overall parameters in the network. To test the discriminative potential of GhoMR, a simple network called GhoMR-Net is constructed using GhoMR modules, and experiments are performed on three public HSI data sets—Indian Pines, University of Pavia, and Salinas Scene. The classification performance is measured using three metrics—overall accuracy (OA), Kappa coefficient (Kappa), and average accuracy (AA). Comparisons with ten state-of-the-art architectures are shown to demonstrate the effectiveness of the method further. Although lightweight, the proposed GhoMR-Net provides comparable or better performance than other networks. The PyTorch code for this study is made available at the iamarijit/GhoMR GitHub repository.

Ultrastructure of developing visual cortical synapses in the cat superior colliculus

1991 ◽  
Vol 49 ◽  
pp. 156-157
Author(s):  
Caroline A. Miller ◽  
Laura L. Bruce
Keyword(s):  
Superior Colliculus ◽  
Phosphate Buffer ◽  
Receptive Fields ◽  
Visual Cortical Area ◽  
Cortical Synapses ◽  
Visual Cortical ◽  
And Function ◽  
Phosphate Buffered Saline ◽  
The Brain ◽  
Cat Superior Colliculus

The first visual cortical axons arrive in the cat superior colliculus by the time of birth. Adultlike receptive fields develop slowly over several weeks following birth. The developing cortical axons go through a sequence of changes before acquiring their adultlike morphology and function. To determine how these axons interact with neurons in the colliculus, cortico-collicular axons were labeled with biocytin (an anterograde neuronal tracer) and studied with electron microscopy.Deeply anesthetized animals received 200-500 nl injections of biocytin (Sigma; 5% in phosphate buffer) in the lateral suprasylvian visual cortical area. After a 24 hr survival time, the animals were deeply anesthetized and perfused with 0.9% phosphate buffered saline followed by fixation with a solution of 1.25% glutaraldehyde and 1.0% paraformaldehyde in 0.1M phosphate buffer. The brain was sectioned transversely on a vibratome at 50 μm. The tissue was processed immediately to visualize the biocytin.

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