Search Results for author:
Found 10 papers, 7 papers with code
Gradient-Congruity Guided Federated Sparse Training
However, FL also faces challenges such as high computational and communication costs regarding resource-constrained devices, and poor generalization performance due to the heterogeneity of data across edge clients and the presence of out-of-distribution data.
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
The out-of-distribution (OOD) problem generally arises when neural networks encounter data that significantly deviates from the training data distribution, i. e., in-distribution (InD).
Ranked #2 on
Out-of-Distribution Detection
on ImageNet-1k vs Textures
(AUROC metric)
M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System
Face presentation attacks (FPA), also known as face spoofing, have brought increasing concerns to the public through various malicious applications, such as financial fraud and privacy leakage.
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning
Specifically, by treating feature elements as neuron activation states, we show that conventional alignment methods tend to deteriorate the diversity of learned invariant features, as they indiscriminately minimize all neuron activation differences.
A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA
On the other hand, pertaining to the implicit knowledge, the multi-modal implicit knowledge for knowledge-based VQA still remains largely unexplored.
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Attention mechanisms are dominating the explainability of deep models.
Answer Questions with Right Image Regions: A Visual Attention Regularization Approach
However, recent studies have pointed out that the highlighted image regions from the visual attention are often irrelevant to the given question and answer, leading to model confusion for correct visual reasoning.
Distilling Knowledge from Pre-trained Language Models via Text Smoothing
As an alternative, we propose a new method for BERT distillation, i. e., asking the teacher to generate smoothed word ids, rather than labels, for teaching the student model in knowledge distillation.
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension
In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Quantifying and Alleviating the Language Prior Problem in Visual Question Answering
Benefiting from the advancement of computer vision, natural language processing and information retrieval techniques, visual question answering (VQA), which aims to answer questions about an image or a video, has received lots of attentions over the past few years.
