Results 71 to 80 of about 115,093 (312)
Contrastive-center loss for deep neural networks
, 2017 The deep convolutional neural network(CNN) has significantly raised the performance of image classification and face recognition. Softmax is usually used as supervision, but it only penalizes the classification loss.
Qi, Ce, Su, Fei
core +1 more source
Graph contrastive learning with implicit augmentations
Neural Networks, 2023 Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, altering certain edges or nodes can unexpectedly change the graph characteristics, and choosing the optimal perturbing ratio for each dataset requires onerous manual tuning. In this
Huidong Liang +5 more
openaire +3 more sources
Dual targeting of RET and SRC synergizes in RET fusion‐positive cancer cells
Molecular Oncology, EarlyView.Despite the strong activity of selective RET tyrosine kinase inhibitors (TKIs), resistance of RET fusion‐positive (RET+) lung cancer and thyroid cancer frequently occurs and is mainly driven by RET‐independent bypass mechanisms. Son et al. show that SRC TKIs significantly inhibit PAK and AKT survival signaling and enhance the efficacy of RET TKIs in ...
Juhyeon Son +13 more
wiley +1 more source
Deep Metric Learning via Lifted Structured Feature Embedding
, 2015 Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolutional neural networks, recent works have shown promising results on ...
Jegelka, Stefanie +3 more
core +1 more source
Molecular Oncology, EarlyView.The cancer problem is increasing globally with projections up to the year 2050 showing unfavourable outcomes in terms of incidence and cancer‐related deaths. The main challenges are prevention, improved therapeutics resulting in increased cure rates and enhanced health‐related quality of life.
Ulrik Ringborg +43 more
wiley +1 more source
Applied Artificial IntelligenceUtilizing self-supervised learning to learn meaningful representations from unlabeled data can be a cost-effective strategy, particularly in medical domains where expert labeling incurs high costs.
Jihyo Kim +6 more
doaj +1 more source
TiCTok: Time-Series Anomaly Detection With Contrastive Tokenization
IEEE Access, 2023 Detecting anomalies in multivariate time-series data is an important task in various real world applications. Recent advances using deep learning have shown promising results in this area.
Minseo Kang, Byunghan Lee
doaj +1 more source
Image Quality Assessment Using Contrastive Learning
IEEE Transactions on Image Processing, 2022 We consider the problem of obtaining image quality representations in a self-supervised manner. We use prediction of distortion type and degree as an auxiliary task to learn features from an unlabeled image dataset containing a mixture of synthetic and realistic distortions.
Pavan C. Madhusudana +4 more
openaire +3 more sources
Molecular Oncology, EarlyView.This study shows that copy number variations (CNVs) can be reliably detected in formalin‐fixed paraffin‐embedded (FFPE) solid cancer samples using ultra‐low‐pass whole‐genome sequencing, provided that key (pre)‐analytical parameters are optimized.
Hanne Goris +10 more
wiley +1 more source