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Found 12 papers, 2 papers with code
Rate-Distortion Theory in Coding for Machines and its Application
To meet this growing demand, several methods have been developed for image and video coding for machines.
Neural Rate Estimator and Unsupervised Learning for Efficient Distributed Image Analytics in Split-DNN Models
Optimizing the pipeline for both compression and task-performance requires high-quality estimates of the information-theoretic rate of the intermediate features.
FRE: A Fast Method For Anomaly Detection And Segmentation
This paper presents a fast and principled approach for solving the visual anomaly detection and segmentation problem.
A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computing
Split computing has emerged as a recent paradigm for implementation of DNN-based AI workloads, wherein a DNN model is split into two parts, one of which is executed on a mobile/client device and the other on an edge-server (or cloud).
Anomalib: A Deep Learning Library for Anomaly Detection
This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization.
Robust Contrastive Active Learning with Feature-guided Query Strategies
We introduce supervised contrastive active learning (SCAL) and propose efficient query strategies in active learning based on the feature similarity (featuresim) and principal component analysis based feature-reconstruction error (fre) to select informative data samples with diverse feature representations.
Mitigating Sampling Bias and Improving Robustness in Active Learning
This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness.
Energy-Based Anomaly Detection and Localization
This brief sketches initial progress towards a unified energy-based solution for the semi-supervised visual anomaly detection and localization problem.
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features
We also show that the feature reconstruction error, which is the $L_2$-norm of the difference between the original feature and the pre-image of its embedding, is highly effective for OOD detection and in some cases superior to the log-likelihood scores.
Tree pyramidal adaptive importance sampling
After each new sample operation, a set of tree leaves are subdivided for improving the approximation of the proposal distribution to the target density.
Deep Probabilistic Models to Detect Data Poisoning Attacks
In the second approach, we use Bayesian deep neural networks trained with mean-field variational inference to estimate model uncertainty associated with the predictions.
Real-time Approximate Bayesian Computation for Scene Understanding
The underlying generative models are built from realistic simulation software, wrapped in a Bayesian error model for the gap between simulation outputs and real data.
