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CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.
[Image source]: GAN-based Anomaly Detection in Imbalance Problems
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning
Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs
Good Practices and A Strong Baseline for Traffic Anomaly Detection
Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows
Anomaly Detection via Reverse Distillation from One-Class Embedding
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
Is Space-Time Attention All You Need for Video Understanding?
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly Detection
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection
TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly Detection
TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.