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Found 8 papers, 1 papers with code
Anomaly Detection via Multi-Scale Contrasted Memory
Deep anomaly detection (AD) aims to provide robust and efficient classifiers for one-class and unbalanced settings.
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks
Our model significantly outperforms state-of-the-art with up to 36% relative error improvement on object anomalies and 40% on face anti-spoofing problems.
Fine-grained Anomaly Detection via Multi-task Self-Supervision
Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields.
Improving Deep Metric Learning with Virtual Classes and Examples Mining
In deep metric learning, the training procedure relies on sampling informative tuples.
DIABLO: Dictionary-based Attention Block for Deep Metric Learning
Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a corresponding metric with deep networks.
Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings
Although the metric learning part is well addressed, this metric is usually computed over the average of the extracted deep features.
Ranked #18 on
Metric Learning
on CUB-200-2011
(using extra training data)
Efficient Codebook and Factorization for Second Order Representation Learning
Learning rich and compact representations is an open topic in many fields such as object recognition or image retrieval.
Leveraging Implicit Spatial Information in Global Features for Image Retrieval
Most image retrieval methods use global features that aggregate local distinctive patterns into a single representation.
