Search Results for author:
Found 5 papers, 2 papers with code
Self-Distilled Representation Learning for Time Series
Self-supervised learning for time-series data holds potential similar to that recently unleashed in Natural Language Processing and Computer Vision.
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation
The STEGO method for unsupervised semantic segmentation contrastively distills feature correspondences of a DINO-pre-trained Vision Transformer and recently set a new state of the art.
MEAL: Manifold Embedding-based Active Learning
Active learning helps learning from small amounts of data by suggesting the most promising samples for labeling.
DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows
We demonstrate the effectiveness of our approach using an ESPNet trained on the Cityscapes dataset as segmentation model, an affine Normalizing Flow as density estimator and use blue noise to ensure homogeneous sampling.
Chameleon: A Semi-AutoML framework targeting quick and scalable development and deployment of production-ready ML systems for SMEs
Developing, scaling, and deploying modern Machine Learning solutions remains challenging for small- and middle-sized enterprises (SMEs).
