Search Results for author:
Found 6 papers, 3 papers with code
Grounding Descriptions in Images informs Zero-Shot Visual Recognition
In this paper, we propose GRAIN, a new pretraining strategy aimed at aligning representations at both fine and coarse levels simultaneously.
Adaptive Memory Replay for Continual Learning
This continual learning (CL) phenomenon has been extensively studied, but primarily in a setting where only a small amount of past data can be stored.
Continual Adaptation of Vision Transformers for Federated Learning
In this paper, we attempt to tackle forgetting and heterogeneity while minimizing overhead costs and without requiring access to any stored data.
A Closer Look at Rehearsal-Free Continual Learning
Next, we explore how to leverage knowledge from a pre-trained model in rehearsal-free continual learning and find that vanilla L2 parameter regularization outperforms EWC parameter regularization and feature distillation.
Robustness through Data Augmentation Loss Consistency
Our experiments show that DAIR consistently outperforms ERM and DA-ERM with little marginal computational cost and sets new state-of-the-art results in several benchmarks involving covariant data augmentation.
Ranked #1 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.2
Multi-domain Dialogue State Tracking
Visual Question Answering
Exploring Weaknesses of VQA Models through Attribution Driven Insights
Deep Neural Networks have been successfully used for the task of Visual Question Answering for the past few years owing to the availability of relevant large scale datasets.
