Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting
Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resource-constrained devices still proved challenging. Therefore, we present an early-exiting model based on nsNet2 that provides several levels of accuracy and resource savings by halting computations at different stages. Moreover, we adapt the original architecture by splitting the information flow to take into account the injected dynamism. We show the trade-offs between performance and computational complexity based on established metrics.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
SmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference
Dynamic early exiting has been proven to improve the inference speed of the pre-trained language model like BERT. However, all samples must go through all consecutive layers before early exiting and more complex samples …
Contrastive LearningLanguage ModellingRTEWNLINSNet: Non-saliency Suppression Sampler for Efficient Video Recognition
It is challenging for artificial intelligence systems to achieve accurate video recognition under the scenario of low computation costs. Adaptive inference based efficient video recognition methods typically preview vide…
Action RecognitionVideo ClassificationVideo RecognitionAmortized-Precision Quantization for Early-Exit Vision Transformers
Vision Transformers (ViTs) achieve strong performance across vision tasks, yet their deployment with low-precision early exiting remains fragile. Existing quantization methods assume static full-depth execution, making t…
DE$^3$-BERT: Distance-Enhanced Early Exiting for BERT based on Prototypical Networks
Early exiting has demonstrated its effectiveness in accelerating the inference of pre-trained language models like BERT by dynamically adjusting the number of layers executed. However, most existing early exiting methods…
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model
Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with increasing model size, resulting in slow infe…
DecoderLanguage ModelingLanguage Modelling