arXiv AI July 29, 2026 By Abhishek dileep, Shubham Sharma, Padmanabhan Rajan

Device Invariance using Domain Adaptation on Acoustic Scene Classification

arXiv:2607. 25887v1 Announce Type: cross Abstract: This paper explores the effectiveness of domain adaptation techniques when using convolutional neural network (CNN)-based and transformer-based feature representations for acoustic scene classification.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 16

Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection

arXiv:2606. 15117v1 Announce Type: cross Abstract: The rapid advancement of generative AI models is leading to more realistic deepfake media, encompassing the manipulation of audio, video, or both.

By Elham Abolhasani, Maryam Ramezani, Hamid R. Rabiee

robotics fine-tuning multimodal safety

Hugging Face Trending Papers

Jun 13

Teacher-Student Structure for Domain Adaptation in Ensemble Audio-Visual Video Deepfake Detection

The rapid advancement of generative AI models is leading to more realistic deepfake media, encompassing the manipulation of audio, video, or both. This raises severe privacy and societal concerns.

robotics fine-tuning multimodal safety

arXiv Machine Learning

Jun 4

Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation

arXiv:2606. 04665v1 Announce Type: new Abstract: Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain.

By Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan

fine-tuning

arXiv AI

Aug 17

Acoustic UAV Detection in Battlefield Scenarios: Handling Noise, Domain Shift, and Weak Labels

arXiv:2608. 14287v1 Announce Type: cross Abstract: Passive acoustic sensing offers a critical, cost-efficient, and, crucially, passive alternative for detecting small unmanned aerial vehicles.

By Vadym Vilhurin, Volodymyr Sydorskyi, Andrii Shevtsov

arXiv AI

Sep 10

AudioFuse: Unified Spectral-Temporal Learning via a Hybrid ViT-1D CNN Architecture for Robust Phonocardiogram Classification

AudioFuse is a hybrid architecture that jointly learns from spectrograms and raw waveforms to classify phonocardiograms. It combines a wide-and-shallow Vision Transformer for spectral features with a shallow 1D CNN for temporal waveforms, reducing overfitting while capturing complementary information. On the PhysioNet 2016 dataset, AudioFuse achieves a state‑of‑the‑art ROC‑AUC of 0.8608 and shows superior robustness to domain shift on the PASCAL dataset, outperforming both spectrogram‑only and waveform‑only baselines.

By Md. Saiful Bari Siddiqui, Utsab Saha

llms computer-vision benchmarks safety

arXiv AI

3d ago

D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Frequency and Pixel Spaces

arXiv:2511.11286v4 Announce Type: replace-cross Abstract: Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision, especially in unsupervised domain adaptation scenario...

By Ruoqi Wang, Haitao Wang, Shaojie Guo, Qiong Luo

computer-vision fine-tuning benchmarks safety

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