Search Results for author:
Found 8 papers, 4 papers with code
Dual Student Networks for Data-Free Model Stealing
To this end, the two main challenges are estimating gradients of the target model without access to its parameters, and generating a diverse set of training samples that thoroughly explores the input space.
CDFSL-V: Cross-Domain Few-Shot Learning for Videos
To address this issue, in this work, we propose a novel cross-domain few-shot video action recognition method that leverages self-supervised learning and curriculum learning to balance the information from the source and target domains.
cross-domain few-shot learning
Few-Shot action recognition
+3
Towards Realistic Semi-Supervised Learning
We also highlight the flexibility of our approach in solving novel class discovery task, demonstrate its stability in dealing with imbalanced data, and complement our approach with a technique to estimate the number of novel classes
Ranked #1 on
Open-World Semi-Supervised Learning
on CIFAR-100
Novel Class Discovery
Open-World Semi-Supervised Learning
+1
OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning
In the open-world SSL problem, the objective is to recognize samples of known classes, and simultaneously detect and cluster samples belonging to novel classes present in unlabeled data.
Ranked #1 on
Open-World Semi-Supervised Learning
on CIFAR-10
Advances in adversarial attacks and defenses in computer vision: A survey
In [2], we reviewed the contributions made by the computer vision community in adversarial attacks on deep learning (and their defenses) until the advent of year 2018.
Towards Consistent Predictive Confidence through Fitted Ensembles
Furthermore, we present a new strong baseline for more consistent predictive confidence in deep models, called fitted ensembles, where overconfident predictions are rectified by transformed versions of the original classification task.
Fitted Learning: Models with Awareness of their Limits
Though deep learning has pushed the boundaries of classification forward, in recent years hints of the limits of standard classification have begun to emerge.
