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Found 7 papers, 3 papers with code
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
Robust Learning Under Label Noise With Iterative Noise-Filtering
We consider the problem of training a model under the presence of label noise.
Consistency-based anomaly detection with adaptive multiple-hypotheses predictions
Thus, due to the lack of representative data, the wide-spread discriminative approaches cannot cover such learning tasks, and rather generative models, which attempt to learn the input density of the normal cases, are used.
Anomaly Detection With Multiple-Hypotheses Predictions
In one-class-learning tasks, only the normal case (foreground) can be modeled with data, whereas the variation of all possible anomalies is too erratic to be described by samples.
Structured Receptive Fields in CNNs
We combine these ideas into structured receptive field networks, a model which has a fixed filter basis and yet retains the flexibility of CNNs.
Latent Hierarchical Model for Activity Recognition
The model is embedded with a latent layer that is able to capture a richer class of contextual information in both state-state and observation-state pairs.
