Search Results for author:
Found 13 papers, 10 papers with code
Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification
Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available data.
Ranked #3 on
Audio Classification
on ICBHI Respiratory Sound Database
(using extra training data)
Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning
While interests in tabular deep learning has significantly grown, conventional tree-based models still outperform deep learning methods.
Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance
In this work, we propose a straightforward approach to augment imbalanced respiratory sound data using an audio diffusion model as a conditional neural vocoder.
Ranked #2 on
Audio Classification
on ICBHI Respiratory Sound Database
(using extra training data)
Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding
To tackle the high inference latency exhibited by autoregressive language models, previous studies have proposed an early-exiting framework that allocates adaptive computation paths for each token based on the complexity of generating the subsequent token.
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification
Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases.
Ranked #1 on
Audio Classification
on ICBHI Respiratory Sound Database
(using extra training data)
Re-thinking Federated Active Learning based on Inter-class Diversity
In this work, we first demonstrate that the superiority of two selector models depends on the global and local inter-class diversity.
Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning
Fortunately, the recent self-supervised learning (SSL) is a promising approach to pretrain a model without annotations, serving as an effective initialization for any downstream tasks.
Self-Contrastive Learning
This paper proposes a novel contrastive learning framework, called Self-Contrastive (SelfCon) Learning, that self-contrasts within multiple outputs from the different levels of a multi-exit network.
Self-Contrastive Learning: Single-viewed Supervised Contrastive Framework using Sub-network
To this end, we propose Self-Contrastive (SelfCon) learning, which self-contrasts within multiple outputs from the different levels of a single network.
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models.
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits
Federated learning has emerged as an innovative paradigm of collaborative machine learning.
MixCo: Mix-up Contrastive Learning for Visual Representation
Contrastive learning has shown remarkable results in recent self-supervised approaches for visual representation.
SIPA: A Simple Framework for Efficient Networks
With the success of deep learning in various fields and the advent of numerous Internet of Things (IoT) devices, it is essential to lighten models suitable for low-power devices.
