68 research outputs found
S-Rocket: Selective Random Convolution Kernels for Time Series Classification
Random convolution kernel transform (Rocket) is a fast, efficient, and novel
approach for time series feature extraction using a large number of independent
randomly initialized 1-D convolution kernels of different configurations. The
output of the convolution operation on each time series is represented by a
partial positive value (PPV). A concatenation of PPVs from all kernels is the
input feature vector to a Ridge regression classifier. Unlike typical deep
learning models, the kernels are not trained and there is no weighted/trainable
connection between kernels or concatenated features and the classifier. Since
these kernels are generated randomly, a portion of these kernels may not
positively contribute in performance of the model. Hence, selection of the most
important kernels and pruning the redundant and less important ones is
necessary to reduce computational complexity and accelerate inference of Rocket
for applications on the edge devices. Selection of these kernels is a
combinatorial optimization problem. In this paper, we propose a scheme for
selecting these kernels while maintaining the classification performance.
First, the original model is pre-trained at full capacity. Then, a population
of binary candidate state vectors is initialized where each element of a vector
represents the active/inactive status of a kernel. A population-based
optimization algorithm evolves the population in order to find a best state
vector which minimizes the number of active kernels while maximizing the
accuracy of the classifier. This activation function is a linear combination of
the total number of active kernels and the classification accuracy of the
pre-trained classifier with the active kernels. Finally, the selected kernels
in the best state vector are utilized to train the Ridge regression classifier
with the selected kernels
Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts
In this paper, we tackle the problem of domain shift. Most existing methods
perform training on multiple source domains using a single model, and the same
trained model is used on all unseen target domains. Such solutions are
sub-optimal as each target domain exhibits its own speciality, which is not
adapted. Furthermore, expecting the single-model training to learn extensive
knowledge from the multiple source domains is counterintuitive. The model is
more biased toward learning only domain-invariant features and may result in
negative knowledge transfer. In this work, we propose a novel framework for
unsupervised test-time adaptation, which is formulated as a knowledge
distillation process to address domain shift. Specifically, we incorporate
Mixture-of-Experts (MoE) as teachers, where each expert is separately trained
on different source domains to maximize their speciality. Given a test-time
target domain, a small set of unlabeled data is sampled to query the knowledge
from MoE. As the source domains are correlated to the target domains, a
transformer-based aggregator then combines the domain knowledge by examining
the interconnection among them. The output is treated as a supervision signal
to adapt a student prediction network toward the target domain. We further
employ meta-learning to enforce the aggregator to distill positive knowledge
and the student network to achieve fast adaptation. Extensive experiments
demonstrate that the proposed method outperforms the state-of-the-art and
validates the effectiveness of each proposed component. Our code is available
at https://github.com/n3il666/Meta-DMoE.Comment: Accepted at NeurIPS202
An End-to-End Network for Co-Saliency Detection in One Single Image
As a common visual problem, co-saliency detection within a single image does
not attract enough attention and yet has not been well addressed. Existing
methods often follow a bottom-up strategy to infer co-saliency in an image,
where salient regions are firstly detected using visual primitives such as
color and shape, and then grouped and merged into a co-saliency map. However,
co-saliency is intrinsically perceived in a complex manner with bottom-up and
top-down strategies combined in human vision. To deal with this problem, a
novel end-to-end trainable network is proposed in this paper, which includes a
backbone net and two branch nets. The backbone net uses ground-truth masks as
top-down guidance for saliency prediction, while the two branch nets construct
triplet proposals for feature organization and clustering, which drives the
network to be sensitive to co-salient regions in a bottom-up way. To evaluate
the proposed method, we construct a new dataset of 2,019 nature images with
co-saliency in each image. Experimental results show that the proposed method
achieves a state-of-the-art accuracy with a running speed of 28fps
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay
Few-shot class-incremental learning (FSCIL) has been proposed aiming to
enable a deep learning system to incrementally learn new classes with limited
data. Recently, a pioneer claims that the commonly used replay-based method in
class-incremental learning (CIL) is ineffective and thus not preferred for
FSCIL. This has, if truth, a significant influence on the fields of FSCIL. In
this paper, we show through empirical results that adopting the data replay is
surprisingly favorable. However, storing and replaying old data can lead to a
privacy concern. To address this issue, we alternatively propose using
data-free replay that can synthesize data by a generator without accessing real
data. In observing the the effectiveness of uncertain data for knowledge
distillation, we impose entropy regularization in the generator training to
encourage more uncertain examples. Moreover, we propose to relabel the
generated data with one-hot-like labels. This modification allows the network
to learn by solely minimizing the cross-entropy loss, which mitigates the
problem of balancing different objectives in the conventional knowledge
distillation approach. Finally, we show extensive experimental results and
analysis on CIFAR-100, miniImageNet and CUB-200 to demonstrate the
effectiveness of our proposed one.Comment: Accepted by ECCV 202
Test-Time Personalization with Meta Prompt for Gaze Estimation
Despite the recent remarkable achievement in gaze estimation, efficient and
accurate personalization of gaze estimation without labels is a practical
problem but rarely touched on in the literature. To achieve efficient
personalization, we take inspiration from the recent advances in Natural
Language Processing (NLP) by updating a negligible number of parameters,
"prompts", at the test time. Specifically, the prompt is additionally attached
without perturbing original network and can contain less than 1% of a
ResNet-18's parameters. Our experiments show high efficiency of the prompt
tuning approach. The proposed one can be 10 times faster in terms of adaptation
speed than the methods compared. However, it is non-trivial to update the
prompt for personalized gaze estimation without labels. At the test time, it is
essential to ensure that the minimizing of particular unsupervised loss leads
to the goals of minimizing gaze estimation error. To address this difficulty,
we propose to meta-learn the prompt to ensure that its updates align with the
goal. Our experiments show that the meta-learned prompt can be effectively
adapted even with a simple symmetry loss. In addition, we experiment on four
cross-dataset validations to show the remarkable advantages of the proposed
method. Code is available at https://github.com/hmarkamcan/TPGaze.Comment: Accepted by AAAI 202
On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture
Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges that are required to be considered. Mobile Crowd Sensing (MCS) embeds three beneficial characteristics: a) cost-effectiveness, b) scalability, and c) mobility and robustness. With the Internet of Things (IoT) becoming a reality, the smart phones are widely becoming available even in remote areas. Hence, both the MCSs characteristics and the plug and play widely available infrastructure provides huge opportunities for the MCS-enabled smart agriculture.opening up several new opportunities at the application level. In this paper, we extensively evaluate the Agriculture Mobile Crowd Sensing
(AMCS) and provide insights for agricultural data collection schemes. In addition, we provide a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low cost requirements. However, we note that AMCSs may still posses limitations in regard to data integrity and quality to be considered as a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns the MCS-enabled agriculture by putting forward six potential applications of AMCS-enabled agriculture. Finally, we propose future research and focus on agricultural characteristics, e.g., seasonality and regionality
Differential expression of miRNAs in colon cancer between African and Caucasian Americans: Implications for cancer racial health disparities
Colorectal cancer (CRC) incidence and mortality are higher in African Americans (AAs) than in Caucasian Americans (CAs) and microRNAs (miRNAs) have been found to be dysregulated in colonic and other neoplasias. The aim of this exploratory study was to identify candidate miRNAs that could contribute to potential biological differences between AA and CA colon cancers. Total RNA was isolated from tumor and paired adjacent normal colon tissue from 30 AA and 31 CA colon cancer patients archived at Stony Brook University (SBU) and Washington University (WU)-St. Louis Medical Center. miRNA profiles were determined by probing human genome-wide miRNA arrays with RNA isolated from each sample. Using repeated measures analysis of variance (RANOVA), miRNAs were selected that exhibited significant (p<0.05) interactions between race and tumor or significant (fold change >1.5, p<0.05) main effects of race and/or tumor. Quantitative polymerase chain reaction (q-PCR) was used to confirm miRNAs identified by microarray analysis. Candidate miRNA targets were analyzed using immunohistochemistry. RANOVA results indicated that miR-182, miR152, miR-204, miR-222 and miR-202 exhibited significant race and tumor main effects. Of these miRNAs, q-PCR analysis confirmed that miR-182 was upregulated in AA vs. CA tumors and exhibited significant race:tumor interaction. Immunohistochemical analysis revealed that the levels of FOXO1 and FOXO3A, two potential miR-182 targets, are reduced in AA tumors. miRNAs may play a role in the differences between AA and CA colon cancer. Specifically, differences in miRNA expression levels of miR-182 may contribute to decreased survival in AA colon cancer patients
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