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Found 13 papers, 0 papers with code
LEURN: Learning Explainable Univariate Rules with Neural Networks
In this paper, we propose LEURN: a neural network architecture that learns univariate decision rules.
Neural Networks are Decision Trees
In this manuscript, we show that any neural network with any activation function can be represented as a decision tree.
A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality
Our second contribution is a denoising loss based on top k-percent of errors in frequency domain.
A Compression Objective and a Cycle Loss for Neural Image Compression
In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss.
Compressing Weight-updates for Image Artifacts Removal Neural Networks
In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible.
Compressibility Loss for Neural Network Weights
In this paper we apply a compressibility loss that enables learning highly compressible neural network weights.
Block-optimized Variable Bit Rate Neural Image Compression
In this work, we propose an end-to-end block-based auto-encoder system for image compression.
Saliency-Enhanced Robust Visual Tracking
In this work, we propose an improvement over DCF based trackers by combining saliency based and other features based filter responses.
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations
We show the effect of l2 normalization on anomaly detection accuracy.
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure
In order to have an in-depth theoretical understanding, in this manuscript, we investigate the graph degree in spectral graph clustering based and kernel based point of views and draw connections to a recent kernel method for the two sample problem.
Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels
By using these encoded images, we train a memory-efficient network using only 0. 048\% of the number of parameters that other deep salient object detection networks have.
INTEL-TUT Dataset for Camera Invariant Color Constancy Research
In this paper, we provide a novel dataset designed for camera invariant color constancy research.
Probabilistic Saliency Estimation
In this paper, we model the salient object detection problem under a probabilistic framework encoding the boundary connectivity saliency cue and smoothness constraints in an optimization problem.
