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Found 11 papers, 7 papers with code
Learning Semantic Similarities for Prototypical Classifiers
Recent metric learning approaches parametrize semantic similarity measures through the use of an encoder trained along with a similarity model, which operates over pairs of representations.
Improving out-of-distribution generalization via multi-task self-supervised pretraining
Self-supervised feature representations have been shown to be useful for supervised classification, few-shot learning, and adversarial robustness.
Ranked #90 on
Domain Generalization
on PACS
An end-to-end approach for the verification problem: learning the right distance
In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder.
Self-supervised representation learning from electroencephalography signals
The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains.
Generalizing to unseen domains via distribution matching
In this work, we tackle such problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions).
Ranked #37 on
Domain Generalization
on PACS
Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment
Besides shedding light on the assumptions that hold for a particular dataset, the estimates of statistical shifts obtained with the proposed approach can be used for investigating other aspects of a machine learning pipeline, such as quantitatively assessing the effectiveness of domain adaptation strategies.
Multi-objective training of Generative Adversarial Networks with multiple discriminators
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary.
Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation
Afterwards, a recurrent model is trained with the goal of providing a sequence of inputs to the previously trained frames generator, thus yielding scenes which look natural.
Deep learning-based electroencephalography analysis: a systematic review
To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.
On-line Adaptative Curriculum Learning for GANs
We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support.
Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch
Non-linear binary classifiers trained on top of our proposed features can achieve a high detection rate (>90%) in a set of white-box attacks and maintain such performance when tested against unseen attacks.
