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Found 6 papers, 4 papers with code
Online Unsupervised Learning of Visual Representations and Categories
Real world learning scenarios involve a nonstationary distribution of classes with sequential dependencies among the samples, in contrast to the standard machine learning formulation of drawing samples independently from a fixed, typically uniform distribution.
Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss
Although deep feedforward neural networks share some characteristics with the primate visual system, a key distinction is their dynamics.
Wandering Within a World: Online Contextualized Few-Shot Learning
We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting.
In Automation We Trust: Investigating the Role of Uncertainty in Active Learning Systems
A current standard policy for AL is to query the oracle (e. g., the analyst) to refine labels for datapoints where the classifier has the highest uncertainty.
MMTM: Multimodal Transfer Module for CNN Fusion
In late fusion, each modality is processed in a separate unimodal Convolutional Neural Network (CNN) stream and the scores of each modality are fused at the end.
Ranked #3 on
Hand Gesture Recognition
on NVGesture
Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails
Although this approach has achieved state-of-the-art results, the deep learning paradigm may be limited due to a reliance on large amounts of annotated training data.
