domain classification

36 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques

kota-dohi/dcase2022_task2_baseline_ae 13 Jun 2022

We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques''.

Domain-knowledge Inspired Pseudo Supervision (DIPS) for Unsupervised Image-to-Image Translation Models to Support Cross-Domain Classification

Hindawi91/Pseudo_Supervised_Metrics 18 Mar 2023

Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain.

Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination


Based on this intuition, in this paper, we devote to distinguishing and exploiting word-level domain contexts for multi-domain NMT.

Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition

hellowangqian/domain-adaptation-capls 25 Mar 2019

Unsupervised domain adaptation aims to transfer knowledge from a source domain to a target domain so that the target domain data can be recognized without any explicit labelling information for this domain.

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization

jlianglab/Fixed-Point-GAN ICCV 2019

Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization.

CUDA: Contradistinguisher for Unsupervised Domain Adaptation

sobalgi/cuda 8 Sep 2019

In this paper, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain.

FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments

achernikova/cybersecurity_evasion 23 Sep 2019

Finally, we demonstrate the potential of performing adversarial training in constrained domains to increase the model resilience against these evasion attacks.