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Found 6 papers, 4 papers with code
Adversarial Denoising Diffusion Model for Unsupervised Anomaly Detection
With the addition of explicit adversarial learning on data samples, ADDM can learn the semantic characteristics of the data more robustly during training, which achieves a similar data sampling performance with much fewer sampling steps than DDPM.
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination
In this paper, we propose Normality-Calibrated Autoencoder (NCAE), which can boost anomaly detection performance on the contaminated datasets without any prior information or explicit abnormal samples in the training phase.
Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification
However, this achievement requires large-scale and well-annotated datasets.
Unsupervised Person Re-identification via Multi-Label Prediction and Classification based on Graph-Structural Insight
The proposed GSMLP and SMLC boost the performance of unsupervised person Re-ID without any pre-labelled dataset.
Unsupervised Vehicle Re-Identification via Self-supervised Metric Learning using Feature Dictionary
The results of DPLM are applied to dictionary-based triplet loss (DTL) to improve the discriminativeness of learnt features and to refine the quality of the results of DPLM progressively.
Boosting Network Weight Separability via Feed-Backward Reconstruction
To this end, we propose an evaluation metric for weight separability based on semi-orthogonality of a matrix and Frobenius distance, and the feed-backward reconstruction loss which explicitly encourages weight separability between the column vectors in the weight matrix.
