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Found 21 papers, 4 papers with code
Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems
The integration of LLMOps into personalized recommendation systems marks a significant advancement in managing LLM-driven applications.
ViTCN: Vision Transformer Contrastive Network For Reasoning
Machine learning models have achieved significant milestones in various domains, for example, computer vision models have an exceptional result in object recognition, and in natural language processing, where Large Language Models (LLM) like GPT can start a conversation with human-like proficiency.
Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis
This paper explores the application of deep learning techniques, particularly focusing on BERT models, in sentiment analysis.
Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations
With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's preferences.
LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models
In addressing the computational and memory demands of fine-tuning Large Language Models(LLMs), we propose LoRA-SP(Streamlined Partial Parameter Adaptation), a novel approach utilizing randomized half-selective parameter freezing within the Low-Rank Adaptation(LoRA)framework.
Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks
However, these defenses are now suffering problems of high inference computational overheads and unfavorable trade-offs between benign accuracy and stealing robustness, which challenges the feasibility of deployed models in practice.
Latent Distribution Adjusting for Face Anti-Spoofing
In this work, we propose a unified framework called Latent Distribution Adjusting (LDA) with properties of latent, discriminative, adaptive, generic to improve the robustness of the FAS model by adjusting complex data distribution with multiple prototypes.
Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework
In this paper, we propose the Adversarial Decoupling Augmentation Framework (ADAF), addressing these issues by targeting the image-text fusion module to enhance the defensive performance of facial privacy protection algorithms.
ICD-Face: Intra-class Compactness Distillation for Face Recognition
Knowledge distillation is an effective model compression method to improve the performance of a lightweight student model by transferring the knowledge of a well-performed teacher model, which has been widely adopted in many computer vision tasks, including face recognition (FR).
Improving Robust Fairness via Balance Adversarial Training
Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes, known as the robust fairness problem.
Universal Backdoor Attacks Detection via Adaptive Adversarial Probe
Most detection methods are designed to verify whether a model is infected with presumed types of backdoor attacks, yet the adversary is likely to generate diverse backdoor attacks in practice that are unforeseen to defenders, which challenge current detection strategies.
DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection
Specifically, we propose the Inverse NMS Clustering (INC) and Rank Matching (RM) to instantiate the dense supervision, without the widely used, conventional sparse pseudo labels.
Robust Face Anti-Spoofing with Dual Probabilistic Modeling
In this work, we attempt to fill this gap by automatically addressing the noise problem from both label and data perspectives in a probabilistic manner.
CoupleFace: Relation Matters for Face Recognition Distillation
In this work, we observe that mutual relation knowledge between samples is also important to improve the discriminative ability of the learned representation of the student model, and propose an effective face recognition distillation method called CoupleFace by additionally introducing the Mutual Relation Distillation (MRD) into existing distillation framework.
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance.
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation
Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively.
One to Transfer All: A Universal Transfer Framework for Vision Foundation Model with Few Data
Transferring with few data in a general way to thousands of downstream tasks is becoming a trend of the foundation model's application.
INTERN: A New Learning Paradigm Towards General Vision
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
Inter-class Discrepancy Alignment for Face Recognition
In this study, we make a key observation that the local con-text represented by the similarities between the instance and its inter-class neighbors1plays an important role forFR.
DAM: Discrepancy Alignment Metric for Face Recognition
To estimate the LID of each face image in the verification process, we propose two types of LID Estimation (LIDE) methods, which are reference-based and learning-based estimation methods, respectively.
Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework
Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters.
