Streaming Anchor Loss: Augmenting Supervision with Temporal Significance

• URL: http://arxiv.org/abs/2310.05886v2
• Date: Thu, 18 Apr 2024 06:11:43 GMT
• Title: Streaming Anchor Loss: Augmenting Supervision with Temporal Significance
• Authors: Utkarsh Oggy Sarawgi, John Berkowitz, Vineet Garg, Arnav Kundu, Minsik Cho, Sai Srujana Buddi, Saurabh Adya, Ahmed Tewfik,
• Abstract summary: Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. We propose a new loss, Streaming Anchor Loss (SAL), to better utilize the given learning capacity by encouraging the model to learn more from essential frames.
• Score: 5.7654216719335105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. Hence, increasing the learning capacity of such streaming models (i.e., by adding more parameters) to improve the predictive power may not be viable for real-world tasks. In this work, we propose a new loss, Streaming Anchor Loss (SAL), to better utilize the given learning capacity by encouraging the model to learn more from essential frames. More specifically, our SAL and its focal variations dynamically modulate the frame-wise cross entropy loss based on the importance of the corresponding frames so that a higher loss penalty is assigned for frames within the temporal proximity of semantically critical events. Therefore, our loss ensures that the model training focuses on predicting the relatively rare but task-relevant frames. Experimental results with standard lightweight convolutional and recurrent streaming networks on three different speech based detection tasks demonstrate that SAL enables the model to learn the overall task more effectively with improved accuracy and latency, without any additional data, model parameters, or architectural changes.

Related papers

• A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation [3.355813093377501]
  Event cameras operate differently from traditional digital cameras, continuously capturing data and generating binary spikes that encode time, location, and light intensity. This necessitates the development of innovative, spike-aware algorithms tailored for event cameras. We propose a purely spike-driven spike transformer network for depth estimation from spiking camera data.
  arXiv  Detail & Related papers  (2024-04-26T11:32:53Z)
• FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments [2.7776688429637466]
  FLARE is a lightweight dual-scheduler FL framework that conditionally transfers training data and deploys models between edge and sensor endpoints. We show that FLARE can significantly reduce the amount of data exchanged between edge and sensor nodes compared to fixed-interval scheduling methods. It can successfully detect concept drift reactively with at least a 16x reduction in latency.
  arXiv  Detail & Related papers  (2023-05-15T10:09:07Z)
• Interference Cancellation GAN Framework for Dynamic Channels [74.22393885274728]
  We introduce an online training framework that can adapt to any changes in the channel. Our framework significantly outperforms recent neural network models on highly dynamic channels.
  arXiv  Detail & Related papers  (2022-08-17T02:01:18Z)
• STIP: A SpatioTemporal Information-Preserving and Perception-Augmented Model for High-Resolution Video Prediction [78.129039340528]
  We propose a Stemporal Information-Preserving and Perception-Augmented Model (STIP) to solve the above two problems. The proposed model aims to preserve thetemporal information for videos during the feature extraction and the state transitions. Experimental results show that the proposed STIP can predict videos with more satisfactory visual quality compared with a variety of state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-09T09:49:04Z)
• From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks [82.21746840893658]
  This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network. We show that while the ResNet-18 model trained on DWT spectrograms achieves a high recognition accuracy, attacking this model is relatively more costly for the adversary.
  arXiv  Detail & Related papers  (2022-04-14T15:14:08Z)
• Real-time Object Detection for Streaming Perception [84.2559631820007]
  Streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception. We build a simple and effective framework for streaming perception. Our method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline.
  arXiv  Detail & Related papers  (2022-03-23T11:33:27Z)
• Learning Fast and Slow for Online Time Series Forecasting [76.50127663309604]
  Fast and Slow learning Networks (FSNet) is a holistic framework for online time-series forecasting. FSNet balances fast adaptation to recent changes and retrieving similar old knowledge. Our code will be made publicly available.
  arXiv  Detail & Related papers  (2022-02-23T18:23:07Z)
• Enabling Continual Learning with Differentiable Hebbian Plasticity [18.12749708143404]
  Continual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge. catastrophic forgetting poses a grand challenge for neural networks performing such learning process. We propose a Differentiable Hebbian Consolidation model which is composed of a Differentiable Hebbian Plasticity.
  arXiv  Detail & Related papers  (2020-06-30T06:42:19Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.