4,347 research outputs found
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning
In this paper, we focus on developing a novel mechanism to preserve
differential privacy in deep neural networks, such that: (1) The privacy budget
consumption is totally independent of the number of training steps; (2) It has
the ability to adaptively inject noise into features based on the contribution
of each to the output; and (3) It could be applied in a variety of different
deep neural networks. To achieve this, we figure out a way to perturb affine
transformations of neurons, and loss functions used in deep neural networks. In
addition, our mechanism intentionally adds "more noise" into features which are
"less relevant" to the model output, and vice-versa. Our theoretical analysis
further derives the sensitivities and error bounds of our mechanism. Rigorous
experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is
highly effective and outperforms existing solutions.Comment: IEEE ICDM 2017 - regular pape
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
Visual crowd counting has been recently studied as a way to enable people
counting in crowd scenes from images. Albeit successful, vision-based crowd
counting approaches could fail to capture informative features in extreme
conditions, e.g., imaging at night and occlusion. In this work, we introduce a
novel task of audiovisual crowd counting, in which visual and auditory
information are integrated for counting purposes. We collect a large-scale
benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of
1,935 images and the corresponding audio clips, and 170,270 annotated
instances. In order to fuse the two modalities, we make use of a linear
feature-wise fusion module that carries out an affine transformation on visual
and auditory features. Finally, we conduct extensive experiments using the
proposed dataset and approach. Experimental results show that introducing
auditory information can benefit crowd counting under different illumination,
noise, and occlusion conditions. The dataset and code will be released. Code
and data have been made availabl
Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations
Extracting generalized and robust representations is a major challenge in
emotion recognition in conversations (ERC). To address this, we propose a
supervised adversarial contrastive learning (SACL) framework for learning
class-spread structured representations. The framework applies contrast-aware
adversarial training to generate worst-case samples and uses a joint
class-spread contrastive learning objective on both original and adversarial
samples. It can effectively utilize label-level feature consistency and retain
fine-grained intra-class features. To avoid the negative impact of adversarial
perturbations on context-dependent data, we design a contextual adversarial
training strategy to learn more diverse features from context and enhance the
model's context robustness. We develop a sequence-based method SACL-LSTM under
this framework, to learn label-consistent and context-robust emotional features
for ERC. Experiments on three datasets demonstrate that SACL-LSTM achieves
state-of-the-art performance on ERC. Extended experiments prove the
effectiveness of the SACL framework.Comment: 16 pages, accepted by ACL 202
UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis
This paper describes our system designed for SemEval-2023 Task 12: Sentiment
analysis for African languages. The challenge faced by this task is the
scarcity of labeled data and linguistic resources in low-resource settings. To
alleviate these, we propose a generalized multilingual system SACL-XLMR for
sentiment analysis on low-resource languages. Specifically, we design a
lexicon-based multilingual BERT to facilitate language adaptation and
sentiment-aware representation learning. Besides, we apply a supervised
adversarial contrastive learning technique to learn sentiment-spread structured
representations and enhance model generalization. Our system achieved
competitive results, largely outperforming baselines on both multilingual and
zero-shot sentiment classification subtasks. Notably, the system obtained the
1st rank on the zero-shot classification subtask in the official ranking.
Extensive experiments demonstrate the effectiveness of our system.Comment: 9 pages, accepted by SemEval@ACL 202
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