Search Results for author:
Found 98 papers, 56 papers with code
Deep Learning for Sensor-based Activity Recognition: A Survey
This paper surveys the recent advance of deep learning based sensor-based activity recognition.
Stratified Transfer Learning for Cross-domain Activity Recognition
The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition.
Cross-position Activity Recognition with Stratified Transfer Learning
STL consists of two components: Stratified Domain Selection (STL-SDS) can select the most similar source domain to the target domain; Stratified Activity Transfer (STL-SAT) is able to perform accurate knowledge transfer.
Balanced Distribution Adaptation for Transfer Learning
To tackle the distribution adaptation problem, in this paper, we propose a novel transfer learning approach, named as Balanced Distribution \underline{A}daptation~(BDA), which can adaptively leverage the importance of the marginal and conditional distribution discrepancies, and several existing methods can be treated as special cases of BDA.
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning.
Ranked #1 on
Domain Adaptation
on Office-Caltech-10
Deep Transfer Learning for Cross-domain Activity Recognition
In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR).
Cross-Domain Activity Recognition
Human Activity Recognition
+1
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning
In this paper, we propose a unified Transfer Channel Pruning (TCP) approach for accelerating UDA models.
Easy Transfer Learning By Exploiting Intra-domain Structures
In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance.
Ranked #4 on
Transfer Learning
on Office-Home
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
It is able to achieve accurate and personalized healthcare without compromising privacy and security.
Transfer Learning with Dynamic Distribution Adaptation
Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions.
Ranked #7 on
Domain Adaptation
on ImageCLEF-DA
Transfer Learning with Dynamic Adversarial Adaptation Network
In this paper, we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domain-invariant representations while quantitatively evaluate the relative importance of global and local domain distributions.
Joint Partial Optimal Transport for Open Set Domain Adaptation
However, in a general setting when the target domain contains classes that are never observed in the source domain, namely in Open Set Domain Adaptation (OSDA), existing DA methods failed to work because of the interference of the extra unknown classes.
Learning to Match Distributions for Domain Adaptation
However, it remains challenging to determine which method is suitable for a given application since they are built with certain priors or bias.
Learning Causal Semantic Representation for Out-of-Distribution Prediction
Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes the semantic factor with the variation factor due to their domain-specific correlation, while only the semantic factor causes the output.
Boosting Adversarial Attacks on Neural Networks with Better Optimizer
However, the success rate of adversarial attacks can be further improved in black-box environments.
Cross-domain Activity Recognition via Substructural Optimal Transport
In this paper, we propose substructure-level matching for domain adaptation (SSDA) to better utilize the locality information of activity data for accurate and efficient knowledge transfer.
Adversarial example generation with AdaBelief Optimizer and Crop Invariance
ABI-FGM and CIM can be readily integrated to build a strong gradient-based attack to further boost the success rates of adversarial examples for black-box attacks.
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition
In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Generalizing to Unseen Domains: A Survey on Domain Generalization
Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.
Learning Invariant Representations across Domains and Tasks
Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledge from the abundant typical pneumonia datasets for COVID-19 image classification.
Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching
End-to-end automatic speech recognition (ASR) can achieve promising performance with large-scale training data.
Ranked #1 on
Cross-environment ASR
on Libri-Adapt
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Exploiting Adapters for Cross-lingual Low-resource Speech Recognition
Based on our previous MetaAdapter that implicitly leverages adapters, we propose a novel algorithms called SimAdapter for explicitly learning knowledge from adapters.
Ranked #1 on
Cross-Lingual ASR
on Common Voice
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare
The success of machine learning applications often needs a large quantity of data.
Deep Subdomain Adaptation Network for Image Classification
The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation.
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals
We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on HAR and HC datasets.
AdaRNN: Adaptive Learning and Forecasting of Time Series
This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data.
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification
In this paper, we propose to view the time series classification problem from the distribution perspective.
Wav2vec-S: Semi-Supervised Pre-Training for Low-Resource ASR
In this work, in order to build a better pre-trained model for low-resource ASR, we propose a pre-training approach called wav2vec-S, where we use task-specific semi-supervised pre-training to refine the self-supervised pre-trained model for the ASR task thus more effectively utilize the capacity of the pre-trained model to generate task-specific representations for ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learning status and learning difficulties of different classes.
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
There is a growing interest in applying machine learning techniques to healthcare.
Margin Calibration for Long-Tailed Visual Recognition
The long-tailed class distribution in visual recognition tasks poses great challenges for neural networks on how to handle the biased predictions between head and tail classes, i. e., the model tends to classify tail classes as head classes.
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection
In this paper, we propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges and enhance the generalization ability in unsupervised anomaly detection.
Multi-Representation Adaptation Network for Cross-domain Image Classification
Based on this, we present Multi-Representation Adaptation Network (MRAN) to accomplish the cross-domain image classification task via multi-representation alignment which can capture the information from different aspects.
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
Training on existing data often makes the model biased towards the distribution of the training data, thus the model might perform terribly on test data with different distributions.
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
Federated learning has attracted increasing attention to building models without accessing the raw user data, especially in healthcare.
Decoupled Federated Learning for ASR with Non-IID Data
Secondly, to reduce the communication and computation costs, we propose decoupled federated learning (DecoupleFL).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Boosting Cross-Domain Speech Recognition with Self-Supervision
The cross-domain performance of automatic speech recognition (ASR) could be severely hampered due to the mismatch between training and testing distributions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Memory-Guided Multi-View Multi-Domain Fake News Detection
In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M$^3$FEND) to address these two challenges.
Domain Generalization for Activity Recognition via Adaptive Feature Fusion
To this end, we propose \emph{Adaptive Feature Fusion for Activity Recognition~(AFFAR)}, a domain generalization approach that learns to fuse the domain-invariant and domain-specific representations to improve the model's generalization performance.
Domain-invariant Feature Exploration for Domain Generalization
Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i. e., the property within a domain, which is agnostic to other domains.
Equivariant Disentangled Transformation for Domain Generalization under Combination Shift
To formally analyze this issue, we provide a unique algebraic formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning.
Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction
Limited labeled data increase the risk of distribution shift between test data and training data.
Domain-Specific Risk Minimization for Out-of-Distribution Generalization
Our bound motivates two strategies to reduce the gap: the first one is ensembling multiple classifiers to enrich the hypothesis space, then we propose effective gap estimation methods for guiding the selection of a better hypothesis for the target.
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
For optimization, we utilize an adapted Mixup to generate an out-of-distribution dataset that can guide the preference direction and optimize with Pareto optimization.
Out-of-Distribution Representation Learning for Time Series Classification
Time series classification is an important problem in real world.
FIXED: Frustratingly Easy Domain Generalization with Mixup
Firstly, Mixup cannot effectively identify the domain and class information that can be used for learning invariant representations.
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective
Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase.
Natural Language Understanding
Out-of-Distribution Generalization
An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
While standard SSL assumes uniform data distribution, we consider a more realistic and challenging setting called imbalanced SSL, where imbalanced class distributions occur in both labeled and unlabeled data.
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective.
FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning
Concretely, we design an attention-based adapter for the large model, CLIP, and the rest operations merely depend on adapters.
Exploring Vision-Language Models for Imbalanced Learning
However, their performance on imbalanced dataset is relatively poor, where the distribution of classes in the training dataset is skewed, leading to poor performance in predicting minority classes.
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
In this paper, we introduce imprecise label learning (ILL), a framework for the unification of learning with various imprecise label configurations.
Ranked #1 on
Learning with noisy labels
on mini WebVision 1.0
Out-of-Distribution Generalization in Text Classification: Past, Present, and Future
Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differs from the training data distribution.
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning
With the constructed self-supervised learning task, DDLearn enlarges the data diversity and explores the latent activity properties.
Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
We have found a new equivalence between two successful methods: selective mixup and resampling.
PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts.
Cross-Lingual Paraphrase Identification
Machine Translation
+5
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences.
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts.
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning
The core idea of RiFT is to exploit the redundant capacity for robustness by fine-tuning the adversarially trained model on its non-robust-critical module.
Frustratingly Easy Model Generalization by Dummy Risk Minimization
Empirical risk minimization (ERM) is a fundamental machine learning paradigm.
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
We propose DIVERSIFY, a general framework, for OOD detection and generalization on dynamic distributions of time series.
From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
Big models, exemplified by Large Language Models (LLMs), are models typically pre-trained on massive data and comprised of enormous parameters, which not only obtain significantly improved performance across diverse tasks but also present emergent capabilities absent in smaller models.
DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks
Moreover, DyVal-generated samples are not only evaluation sets, but also helpful data for fine-tuning to improve the performance of LLMs on existing benchmarks.
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
This paper aims to understand the nature of noise in pre-training datasets and to mitigate its impact on downstream tasks.
Meta Semantic Template for Evaluation of Large Language Models
We hope this initial work can shed light on future research of LLMs evaluation.
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources.
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language Models (LLMs).
A Survey of Heterogeneous Transfer Learning
These methods typically presuppose identical feature spaces and label spaces in both domains, known as homogeneous transfer learning, which, however, is not always a practical assumption.
CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents
Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning.
Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition
However, the high computational cost of optimization-based TTA algorithms makes it intractable to run on resource-constrained edge devices.
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
We propose a conceptually simple and lightweight framework for improving the robustness of vision models through the combination of knowledge distillation and data augmentation.
Ranked #13 on
Domain Generalization
on ImageNet-Sketch
Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning
Hence, we propose fine-tuning the parameters of the attention pooling layer during the training process to encourage the model to focus on task-specific semantics.
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation.
PromptBench: A Unified Library for Evaluation of Large Language Models
The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks.
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it.
Supervised Knowledge Makes Large Language Models Better In-context Learners
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering.
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization
In this study, we address this gap by introducing SpecFormer, specifically designed to enhance ViTs' resilience against adversarial attacks, with support from carefully derived theoretical guarantees.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Large Language Model Evaluation via Matrix Entropy
Large language models (LLMs) have revolutionized the field of natural language processing, extending their strong capabilities into multi-modal domains.
On Catastrophic Inheritance of Large Foundation Models
Large foundation models (LFMs) are claiming incredible performances.
A General Framework for Learning from Weak Supervision
Weakly supervised learning generally faces challenges in applicability to various scenarios with diverse weak supervision and in scalability due to the complexity of existing algorithms, thereby hindering the practical deployment.
Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
Time series analysis is essential for comprehending the complexities inherent in various real-world systems and applications.
Open-Vocabulary Calibration for Vision-Language Models
Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few.
DyVal 2: Dynamic Evaluation of Large Language Models by Meta Probing Agents
Our multifaceted analysis demonstrated the strong correlation between the basic abilities and an implicit Matthew effect on model size, i. e., larger models possess stronger correlations of the abilities.
MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms
Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces.
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness.
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting
Time-series forecasting (TSF) finds broad applications in real-world scenarios.
Adversarial example soups: averaging multiple adversarial examples improves transferability without increasing additional generation time
For transfer-based attacks, the adversarial examples are crafted on the surrogate model, which can be implemented to mislead the target model effectively.
NPHardEval4V: A Dynamic Reasoning Benchmark of Multimodal Large Language Models
Understanding the reasoning capabilities of Multimodal Large Language Models (MLLMs) is an important area of research.
ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models
Our key idea is to construct questions using the database schema, records, and functional dependencies such that they can be automatically verified.
Learning with Noisy Foundation Models
Foundation models are usually pre-trained on large-scale datasets and then adapted to downstream tasks through tuning.
Detoxifying Large Language Models via Knowledge Editing
This paper investigates using knowledge editing techniques to detoxify Large Language Models (LLMs).
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models
The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-effectively while ensuring reliability, reproducibility, and efficiency.
