50 research outputs found
Improving Multi-Scale Aggregation Using Feature Pyramid Module for Robust Speaker Verification of Variable-Duration Utterances
Currently, the most widely used approach for speaker verification is the deep
speaker embedding learning. In this approach, we obtain a speaker embedding
vector by pooling single-scale features that are extracted from the last layer
of a speaker feature extractor. Multi-scale aggregation (MSA), which utilizes
multi-scale features from different layers of the feature extractor, has
recently been introduced and shows superior performance for variable-duration
utterances. To increase the robustness dealing with utterances of arbitrary
duration, this paper improves the MSA by using a feature pyramid module. The
module enhances speaker-discriminative information of features from multiple
layers via a top-down pathway and lateral connections. We extract speaker
embeddings using the enhanced features that contain rich speaker information
with different time scales. Experiments on the VoxCeleb dataset show that the
proposed module improves previous MSA methods with a smaller number of
parameters. It also achieves better performance than state-of-the-art
approaches for both short and long utterances.Comment: Accepted to Interspeech 202
TiDAL: Learning Training Dynamics for Active Learning
Active learning (AL) aims to select the most useful data samples from an
unlabeled data pool and annotate them to expand the labeled dataset under a
limited budget. Especially, uncertainty-based methods choose the most uncertain
samples, which are known to be effective in improving model performance.
However, AL literature often overlooks training dynamics (TD), defined as the
ever-changing model behavior during optimization via stochastic gradient
descent, even though other areas of literature have empirically shown that TD
provides important clues for measuring the sample uncertainty. In this paper,
we propose a novel AL method, Training Dynamics for Active Learning (TiDAL),
which leverages the TD to quantify uncertainties of unlabeled data. Since
tracking the TD of all the large-scale unlabeled data is impractical, TiDAL
utilizes an additional prediction module that learns the TD of labeled data. To
further justify the design of TiDAL, we provide theoretical and empirical
evidence to argue the usefulness of leveraging TD for AL. Experimental results
show that our TiDAL achieves better or comparable performance on both balanced
and imbalanced benchmark datasets compared to state-of-the-art AL methods,
which estimate data uncertainty using only static information after model
training.Comment: ICCV 2023 Camera-Read
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
Recent studies on learning with noisy labels have shown remarkable
performance by exploiting a small clean dataset. In particular, model agnostic
meta-learning-based label correction methods further improve performance by
correcting noisy labels on the fly. However, there is no safeguard on the label
miscorrection, resulting in unavoidable performance degradation. Moreover,
every training step requires at least three back-propagations, significantly
slowing down the training speed. To mitigate these issues, we propose a robust
and efficient method that learns a label transition matrix on the fly.
Employing the transition matrix makes the classifier skeptical about all the
corrected samples, which alleviates the miscorrection issue. We also introduce
a two-head architecture to efficiently estimate the label transition matrix
every iteration within a single back-propagation, so that the estimated matrix
closely follows the shifting noise distribution induced by label correction.
Extensive experiments demonstrate that our approach shows the best performance
in training efficiency while having comparable or better accuracy than existing
methods.Comment: ECCV202
A contrasting function for miR-137 in embryonic mammogenesis and adult breast carcinogenesis
published_or_final_versio
Treatment-Seeking Behaviors and Related Epidemiological Features in Korean Acne Patients
Little is known about the treatment-seeking behaviors of acne patients, especially Asian acne patients. This study was performed to obtain detailed information about the treatment-seeking behaviors in Korean acne patients. Patients who visited the dermatology departments at 17 university hospitals completed a self-administered questionnaire. Most patients obtained information about acne from doctors or the Internet. The most important criteria for selecting a treatment method or choosing a particular clinic were effectiveness and accessibility. Patients used traditional medicine, visited beauty clinics, drank more water, and used over-the-counter topical agents more frequently than they sought doctors during the worsening period. The degree of satisfaction in treatment was found to depend on the total cost of treatment, number of places visited, site affected by acne, and emotional stress. Those who had experienced a side effect tended to have been treated for longer, to have paid more for treatment, and to have an associated skin disease. Treatments prescribed by dermatology clinics had the lowest aggravating rate, although improvement rates for family medicine clinics were also fairly high. This is the first study to investigate in detail the demographic features and characteristics of the treatment-seeking behaviors of acne patients in Asia