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Ternary and Binary Quantization for Improved Classification
Furthermore, this quantization property could be maintained in the random projections of sparse features, if both the features and random projection matrices are sufficiently sparse.
Cascaded Compressed Sensing Networks: A Reversible Architecture for Layerwise Learning
In the letter, we show that target propagation could be achieved by modeling the network s each layer with compressed sensing, without the need of auxiliary networks.
Deep Learning to Ternary Hash Codes by Continuation
Recently, it has been observed that {0, 1,-1}-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform {-1, 1}-binary codes in image retrieval.
Learning Actions from Human Demonstration Video for Robotic Manipulation
However, the general video captioning methods focus more on the understanding of the full frame, lacking of consideration on the specific object of interests in robotic manipulations.
Sparse Matrix-based Random Projection for Classification
As a typical dimensionality reduction technique, random projection can be simply implemented with linear projection, while maintaining the pairwise distances of high-dimensional data with high probability.
