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Found 60 papers, 20 papers with code
FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly Generation and Discrimination
Nowadays large computers extensively output logs to record the runtime status and it has become crucial to identify any suspicious or malicious activities from the information provided by the realtime logs.
Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization
Visual anomaly detection is a challenging open-set task aimed at identifying unknown anomalous patterns while modeling normal data.
SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic
Scalable service-Oriented Middleware over IP (SOME/IP) is an Ethernet communication standard protocol in the Automotive Open System Architecture (AUTOSAR), promoting ECU-to-ECU communication over the IP stack.
Adaptive Confidence Multi-View Hashing for Multimedia Retrieval
Furthermore, an adaptive confidence multi-view network is employed to measure the confidence of each view and then fuse multi-view features through a weighted summation.
Domain Generalization of 3D Object Detection by Density-Resampling
Specifically, from the perspective of data augmentation, we design a universal physical-aware density-based data augmentation (PDDA) method to mitigate the performance loss stemming from diverse point densities.
MyriadAL: Active Few Shot Learning for Histopathology
To this end, we introduce an active few shot learning framework, Myriad Active Learning (MAL), including a contrastive-learning encoder, pseudo-label generation, and novel query sample selection in the loop.
Improving Contrastive Learning of Sentence Embeddings with Focal-InfoNCE
The recent success of SimCSE has greatly advanced state-of-the-art sentence representations.
Text-driven Prompt Generation for Vision-Language Models in Federated Learning
Prompt learning for vision-language models, e. g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learning due to computational reasons.
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization
On top of the proposed AnoCLIP, we further introduce a test-time adaptation (TTA) mechanism to refine visual anomaly localization results, where we optimize a lightweight adapter in the visual encoder using AnoCLIP's pseudo-labels and noise-corrupted tokens.
Advancing Adversarial Robustness Through Adversarial Logit Update
Based on ALU, we introduce a new classification paradigm that utilizes pre- and post-purification logit differences for model's adversarial robustness boost.
GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement
We attribute this deficiency to the lack of ability to extract the geometric layout of visual features and models' overfitting to low-level details.
Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning
Our approach succeeds in performance while significantly saving inference steps.
Boosting Multi-modal Model Performance with Adaptive Gradient Modulation
In addition, we find that the jointly trained model typically has a preferred modality on which the competition is weaker than other modalities.
G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima
Deep neural networks (DNNs) have demonstrated promising results in various complex tasks.
AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning
Continual lifelong learning is an machine learning framework inspired by human learning, where learners are trained to continuously acquire new knowledge in a sequential manner.
BMAD: Benchmarks for Medical Anomaly Detection
However, there is a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain.
Inspire creativity with ORIBA: Transform Artists' Original Characters into Chatbots through Large Language Model
This research delves into the intersection of illustration art and artificial intelligence (AI), focusing on how illustrators engage with AI agents that embody their original characters (OCs).
DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture
Additionally, under the same experimental conditions using the same set of training and testing datasets, DPSeq surpassed 4 CNN (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and 2 transformer (ViT and Swin-T) models, achieving the highest AUROC and AUPRC values in predicting MSI status, BRAF mutation, and CIMP status.
Synthetic Datasets for Autonomous Driving: A Survey
In this paper, we summarize the evolution of synthetic dataset generation methods and review the work to date in synthetic datasets related to single and multi-task categories for to autonomous driving study.
Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data
Recently, various deep learning methods have shown significant successes in medical image analysis, especially in the detection of cancer metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSIs).
Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines
However, most digital pathology artificial-intelligence models are based on CNN architectures, probably owing to a lack of data regarding NLP models for pathology images.
How To Prevent the Poor Performance Clients for Personalized Federated Learning?
Intuitively, these poor clients may come from biased universal information shared with others.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence
We attribute this deficiency to the lack of ability to extract the spatial configuration of visual feature layouts and models' overfitting on low-level details from the training set.
Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting
Deep Neural Networks are vulnerable to adversarial attacks.
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement
Then, for the first attempt, we construct a benchmark based on the physical-aware common corruptions for point cloud detectors, which contains a total of 1, 122, 150 examples covering 7, 481 scenes, 25 common corruption types, and 6 severities.
Prognostic Significance of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images in Colorectal Cancers
Methods We developed an automated, multiscale LinkNet workflow for quantifying cellular-level TILs for CRC tumors using H&E-stained images.
Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: Achieving SOTA predictive performance with fewer data using Swin Transformer
In this study, based on the latest Hierarchical Vision Transformer using Shifted Windows (Swin-T), we developed an efficient workflow for biomarkers in CRC (MSI, hypermutation, chromosomal instability, CpG island methylator phenotype, BRAF, and TP53 mutation) that only required relatively small datasets, but achieved the state-of-the-art (SOTA) predictive performance.
Generalized Federated Learning via Sharpness Aware Minimization
Therefore, in this paper, we revisit the solutions to the distribution shift problem in FL with a focus on local learning generality.
A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations
In addition, compared to the published models for genetic alterations, AMIML provided a significant improvement for predicting a wide range of genes (e. g., KMT2C, TP53, and SETD2 for KIRC; ERBB2, BRCA1, and BRCA2 for BRCA; JAK1, POLE, and MTOR for UCEC) as well as produced outstanding predictive models for other clinically relevant gene mutations, which have not been reported in the current literature.
Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners
Based on digital pathology slice scanning technology, artificial intelligence algorithms represented by deep learning have achieved remarkable results in the field of computational pathology.
DDDM: a Brain-Inspired Framework for Robust Classification
The mechanism is well described by the Drift-Diffusion Model (DDM).
SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities
Our experiments suggest that for some tasks where different modalities are complementary, the multi-modal models still tend to use the dominant modality alone and ignore the cooperation across modalities.
Adversarial Fine-tune with Dynamically Regulated Adversary
Adversarial training is an effective method to boost model robustness to malicious, adversarial attacks.
Colorectal cancer survival prediction using deep distribution based multiple-instance learning
Several deep learning algorithms have been developed to predict survival of cancer patients using whole slide images (WSIs). However, identification of image phenotypes within the WSIs that are relevant to patient survival and disease progression is difficult for both clinicians, and deep learning algorithms.
Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering
We showed that unsupervised clustering of image patches could help identify predictive patches, exclude patches lack of predictive information, and therefore improve prediction on gene mutations in all three different cancer types, compared with the WSI based method without selection of image patches and models based on only tumor regions.
Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects
We consider the construction of confidence intervals for treatment effects estimated using panel models with interactive fixed effects.
Anomaly Detection via Reverse Distillation from One-Class Embedding
Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD.
Ranked #2 on
Anomaly Detection
on AeBAD-V
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network.
Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model
The extracted features from the finetuned FTX2048 exhibited significantly higher accuracy for predicting tisue types of CRC compared to the off the shelf feature directly from Xception based on ImageNet database.
Generating Diverse and Natural 3D Human Motions From Text
Automated generation of 3D human motions from text is a challenging problem.
Ranked #6 on
Motion Synthesis
on InterHuman
FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation
Federated Learning (FL) is a decentralized machine learning architecture, which leverages a large number of remote devices to learn a joint model with distributed training data.
Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks
Despite its great success, deep learning severely suffers from robustness; that is, deep neural networks are very vulnerable to adversarial attacks, even the simplest ones.
A Retrospective Analysis using Deep-Learning Models for Prediction of Survival Outcome and Benefit of Adjuvant Chemotherapy in Stage II/III Colorectal Cancer
We developed a novel deep-learning algorithm (CRCNet) using whole-slide images from Molecular and Cellular Oncology (MCO) to predict survival benefit of adjuvant chemotherapy in stage II/III CRC.
CoverTheFace: face covering monitoring and demonstrating using deep learning and statistical shape analysis
For images where faces are improperly covered, our mask overlay module incorporates statistical shape analysis (SSA) and dense landmark alignment to approximate the geometry of a face and generates corresponding face-covering examples.
Object Wake-up: 3D Object Rigging from a Single Image
A dataset of generic 3D objects with ground-truth annotated skeletons is collected.
Fuzzy Expert Systems for Prediction of ICU Admission in Patients with COVID-19
The results show that the type-2 fuzzy expert system and ANFIS models perform competitively in terms of accuracy and F-measure compared to the other system modeling techniques.
On the limits of algorithmic prediction across the globe
We tested how well predictive models of human behavior trained in a developed country generalize to people in less developed countries by modeling global variation in 200 predictors of academic achievement on nationally representative student data for 65 countries.
Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients
Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices.
Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images
Prior model-based stain separation methods usually rely on stains' spatial distributions over an image and may fail to solve the co-localization problem.
Research Replication Prediction Using Weakly Supervised Learning
In this paper, we propose two weakly supervised learning approaches that use automatically extracted text information of research papers to improve the prediction accuracy of research replication using both labeled and unlabeled datasets.
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
This high-quality sample sieve allows us to treat clean examples and the corrupted ones separately in training a DNN solution, and such a separation is shown to be advantageous in the instance-dependent noise setting.
Image Classification with Label Noise
Learning with noisy labels
Stain Style Transfer of Histopathology Images Via Structure-Preserved Generative Learning
To address the issue of color variations in histopathology images, this study proposes two stain style transfer models, SSIM-GAN and DSCSI-GAN, based on the generative adversarial networks.
How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To Pathology Images?
Particularly, compared to the performance baseline obtained by random-weight model, though transferability of off-the-shelf representations from deep layers heavily depend on specific pathology image sets, the general representation generated by early layers does convey transferred knowledge in various image classification applications.
Sample Elicitation
We also show a connection between this sample elicitation problem and $f$-GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples.
L2-Hypocoercivity and large time asymptotics of the linearized Vlasov-Poisson-Fokker-Planck system
This paper is devoted to the linearized Vlasov-Poisson-Fokker-Planck system in presence of an external potential of confinement.
Analysis of PDEs 82C40, 35H10, 35P15, 35Q84, 35R09, 47G20, 82C21, 82D10, 82D37
Analysis of the Synergy between Modularity and Autonomy in an Artificial Intelligence Based Fleet Competition
A novel approach is provided for evaluating the benefits and burdens from vehicle modularity in fleets/units through the analysis of a game theoretical model of the competition between autonomous vehicle fleets in an attacker-defender game.
Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder
We apply the proposed method to a public breast cancer image set.
End-to-End Open-Domain Question Answering with BERTserini
We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit.
Ranked #4 on
Open-Domain Question Answering
on SQuAD1.1 dev
Analysis of Fleet Modularity in an Artificial Intelligence-Based Attacker-Defender Game
Because combat environments change over time and technology upgrades are widespread for ground vehicles, a large number of vehicles and equipment become quickly obsolete.
