153 research outputs found
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction
A common challenge in personalized user preference prediction is the
cold-start problem. Due to the lack of user-item interactions, directly
learning from the new users' log data causes serious over-fitting problem.
Recently, many existing studies regard the cold-start personalized preference
prediction as a few-shot learning problem, where each user is the task and
recommended items are the classes, and the gradient-based meta learning method
(MAML) is leveraged to address this challenge. However, in real-world
application, the users are not uniformly distributed (i.e., different users may
have different browsing history, recommended items, and user profiles. We
define the major users as the users in the groups with large numbers of users
sharing similar user information, and other users are the minor users),
existing MAML approaches tend to fit the major users and ignore the minor
users. To address this cold-start task-overfitting problem, we propose a novel
personalized adaptive meta learning approach to consider both the major and the
minor users with three key contributions: 1) We are the first to present a
personalized adaptive learning rate meta-learning approach to improve the
performance of MAML by focusing on both the major and minor users. 2) To
provide better personalized learning rates for each user, we introduce a
similarity-based method to find similar users as a reference and a tree-based
method to store users' features for fast search. 3) To reduce the memory usage,
we design a memory agnostic regularizer to further reduce the space complexity
to constant while maintain the performance. Experiments on MovieLens,
BookCrossing, and real-world production datasets reveal that our method
outperforms the state-of-the-art methods dramatically for both the minor and
major users.Comment: Preprint Versio
Bridging the Domain Gap for Multi-Agent Perception
Existing multi-agent perception algorithms usually select to share deep
neural features extracted from raw sensing data between agents, achieving a
trade-off between accuracy and communication bandwidth limit. However, these
methods assume all agents have identical neural networks, which might not be
practical in the real world. The transmitted features can have a large domain
gap when the models differ, leading to a dramatic performance drop in
multi-agent perception. In this paper, we propose the first lightweight
framework to bridge such domain gaps for multi-agent perception, which can be a
plug-in module for most existing systems while maintaining confidentiality. Our
framework consists of a learnable feature resizer to align features in multiple
dimensions and a sparse cross-domain transformer for domain adaption. Extensive
experiments on the public multi-agent perception dataset V2XSet have
demonstrated that our method can effectively bridge the gap for features from
different domains and outperform other baseline methods significantly by at
least 8% for point-cloud-based 3D object detection.Comment: Accepted by ICRA2023.Code: https://github.com/DerrickXuNu/MPD
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
Though deep learning-based object detection methods have achieved promising
results on the conventional datasets, it is still challenging to locate objects
from the low-quality images captured in adverse weather conditions. The
existing methods either have difficulties in balancing the tasks of image
enhancement and object detection, or often ignore the latent information
beneficial for detection. To alleviate this problem, we propose a novel
Image-Adaptive YOLO (IA-YOLO) framework, where each image can be adaptively
enhanced for better detection performance. Specifically, a differentiable image
processing (DIP) module is presented to take into account the adverse weather
conditions for YOLO detector, whose parameters are predicted by a small
convolutional neural net-work (CNN-PP). We learn CNN-PP and YOLOv3 jointly in
an end-to-end fashion, which ensures that CNN-PP can learn an appropriate DIP
to enhance the image for detection in a weakly supervised manner. Our proposed
IA-YOLO approach can adaptively process images in both normal and adverse
weather conditions. The experimental results are very encouraging,
demonstrating the effectiveness of our proposed IA-YOLO method in both foggy
and low-light scenarios.Comment: AAAI 2022, Preprint version with Appendi
Domain Adaptation based Enhanced Detection for Autonomous Driving in Foggy and Rainy Weather
Typically, object detection methods for autonomous driving that rely on
supervised learning make the assumption of a consistent feature distribution
between the training and testing data, however such assumption may fail in
different weather conditions. Due to the domain gap, a detection model trained
under clear weather may not perform well in foggy and rainy conditions.
Overcoming detection bottlenecks in foggy and rainy weather is a real challenge
for autonomous vehicles deployed in the wild. To bridge the domain gap and
improve the performance of object detectionin foggy and rainy weather, this
paper presents a novel framework for domain-adaptive object detection. The
adaptations at both the image-level and object-level are intended to minimize
the differences in image style and object appearance between domains.
Furthermore, in order to improve the model's performance on challenging
examples, we introduce a novel adversarial gradient reversal layer that
conducts adversarial mining on difficult instances in addition to domain
adaptation. Additionally, we suggest generating an auxiliary domain through
data augmentation to enforce a new domain-level metric regularization.
Experimental findings on public V2V benchmark exhibit a substantial enhancement
in object detection specifically for foggy and rainy driving scenarios.Comment: only change the title of this pape
Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather
Most object detection methods for autonomous driving usually assume a
consistent feature distribution between training and testing data, which is not
always the case when weathers differ significantly. The object detection model
trained under clear weather might not be effective enough in foggy weather
because of the domain gap. This paper proposes a novel domain adaptive object
detection framework for autonomous driving under foggy weather. Our method
leverages both image-level and object-level adaptation to diminish the domain
discrepancy in image style and object appearance. To further enhance the
model's capabilities under challenging samples, we also come up with a new
adversarial gradient reversal layer to perform adversarial mining for the hard
examples together with domain adaptation. Moreover, we propose to generate an
auxiliary domain by data augmentation to enforce a new domain-level metric
regularization. Experimental results on public benchmarks show the
effectiveness and accuracy of the proposed method. The code is available at
https://github.com/jinlong17/DA-Detect.Comment: Accepted by WACV2023. Code is available at
https://github.com/jinlong17/DA-Detec
A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data
BACKGROUND: Tandem mass spectrometry (MS/MS) is a powerful tool for protein identification. Although great efforts have been made in scoring the correlation between tandem mass spectra and an amino acid sequence database, improvements could be made in three aspects, including characterization ofpeaks in spectra, adoption of effective scoring functions and access to thereliability of matching between peptides and spectra. RESULTS: A novel scoring function is presented, along with criteria to estimate the performance confidence of the function. Through learning the typesof product ions and the probability of generating them, a hypothetic spectrum was generated for each candidate peptide. Then relative entropy was introduced to measure the similarity between the hypothetic and the observed spectra. Based on the extreme value distribution (EVD) theory, a threshold was chosen to distinguish a true peptide assignment from a random one. Tests on a public MS/MS dataset demonstrated that this method performs better than the well-known SEQUEST. CONCLUSION: A reliable identification of proteins from the spectra promises a more efficient application of tandem mass spectrometry to proteomes with high complexity
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