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Found 8 papers, 2 papers with code
Efficient Verification-Based Face Identification
We study the problem of performing face verification with an efficient neural model $f$.
Anomaly Detection with Variance Stabilized Density Estimation
Then, we design a variance stabilized density estimation problem for maximizing the likelihood of the observed samples while minimizing the variance of the density around normal samples.
Revisiting the Noise Model of Stochastic Gradient Descent
Following the central limit theorem, SGN was initially modeled as Gaussian, and lately, it has been suggested that stochastic gradient noise is better characterized using $S\alpha S$ L\'evy distribution.
Domain-Generalizable Multiple-Domain Clustering
This work generalizes the problem of unsupervised domain generalization to the case in which no labeled samples are available (completely unsupervised).
Mixing between the Cross Entropy and the Expectation Loss Terms
The cross entropy loss is widely used due to its effectiveness and solid theoretical grounding.
Feature Whitening via Gradient Transformation for Improved Convergence
Compared to EVD, complexity is reduced by a factor of the input feature dimension M. We exemplify the proposed algorithms with ResNet-based networks for image classification demonstrated on the CIFAR and Imagenet datasets.
Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain
In this paper we are approaching the task in a completely different way; we are looking at the data from the compressed stream as a one unit clip and propose that the residual frames can replace the original RGB frames from the raw domain.
Adaptive and Iteratively Improving Recurrent Lateral Connections
The current leading computer vision models are typically feed forward neural models, in which the output of one computational block is passed to the next one sequentially.
