Search Results for author:
Found 6 papers, 1 papers with code
Preventing Catastrophic Forgetting through Memory Networks in Continuous Detection
Modern pre-trained architectures struggle to retain previous information while undergoing continuous fine-tuning on new tasks.
TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Models
Text-to-Image (TTI) generative models have shown great progress in the past few years in terms of their ability to generate complex and high-quality imagery.
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision
In this work, we present a practical setting of inductive zero and few-shot learning, where unlabeled images from other out-of-data classes, that do not belong to seen or unseen categories, can be used to improve generalization in any-shot learning.
Attentive Recurrent Tensor Model for Community Question Answering
Further, we use the tensor parameters to introduce a 3-way interaction between question, answer and external features in vector space.
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification
We present a novel idea that combines the neural, statistical and external features to provide an efficient solution to this problem.
Ranked #3 on
Fake News Detection
on FNC-1
Common Representation Learning Using Step-based Correlation Multi-Modal CNN
In a broader perspective, the techniques used to investigate common representation learning falls under the categories of canonical correlation-based approaches and autoencoder based approaches.
