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Found 10 papers, 5 papers with code
Deep Reversible Consistency Learning for Cross-modal Retrieval
Then, RSC employs a Modality-invariant Representation Recasting mechanism (MRR) to recast the potential modality-invariant representations from sample semantic labels by the generalized inverse matrix of the prior.
Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels
Inspired by human cognitive learning, a few methods introduce self-paced learning (SPL) to gradually train the model from easy to hard samples, which is often used to mitigate the effects of feature noise or outliers.
Fuzzy Multimodal Learning for Trusted Cross-modal Retrieval
However, directly optimizing the category credibility could mislead the model by over-optimizing the necessity for unmatched categories.
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection
Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications.
Time Series Data Augmentation for Deep Learning: A Survey
In this paper, we systematically review different data augmentation methods for time series.
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks
It is deployed as a public online service and widely adopted in different business scenarios at Alibaba Group.
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection.
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods.
Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee
This bound is sub-optimal comparing to the information theoretical lower bound $\mathcal{O}(kd)$.
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.
