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Found 7 papers, 1 papers with code
SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy Minimization
Automatic speech recognition (ASR) models are frequently exposed to data distribution shifts in many real-world scenarios, leading to erroneous predictions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Diffusion Video Autoencoders: Toward Temporally Consistent Face Video Editing via Disentangled Video Encoding
Inspired by the impressive performance of recent face image editing methods, several studies have been naturally proposed to extend these methods to the face video editing task.
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation
Deep neural networks (DNNs), despite their impressive ability to generalize over-capacity networks, often rely heavily on malignant bias as shortcuts instead of task-related information for discriminative tasks.
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation
Graph-structured datasets usually have irregular graph sizes and connectivities, rendering the use of recent data augmentation techniques, such as Mixup, difficult.
Mutually-Constrained Monotonic Multihead Attention for Online ASR
Despite the feature of real-time decoding, Monotonic Multihead Attention (MMA) shows comparable performance to the state-of-the-art offline methods in machine translation and automatic speech recognition (ASR) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding
We consider the problem of active feature acquisition where the goal is to sequentially select the subset of features in order to achieve the maximum prediction performance in the most cost-effective way at test time.
Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification
We consider the problem of active feature acquisition, where we sequentially select the subset of features in order to achieve the maximum prediction performance in the most cost-effective way.
