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Found 19 papers, 7 papers with code
Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation
The Segment Anything Model (SAM) gained significant success in natural image segmentation, and many methods have tried to fine-tune it to medical image segmentation.
Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning
3D medical image segmentation is a challenging task with crucial implications for disease diagnosis and treatment planning.
Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry
In such a way, FL is implemented as a privacy-enhancing collaborative learning technique that addresses the challenges posed by the sensitivity and privacy of data in traditional machine learning solutions.
DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Content
This paper explores the image synthesis capabilities of GPT-4, a leading multi-modal large language model.
MocFormer: A Two-Stage Pre-training-Driven Transformer for Drug-Target Interactions Prediction
Drug-target interactions (DTIs) is essential for advancing pharmaceuticals.
DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation
Few-shot medical image semantic segmentation is of paramount importance in the domain of medical image analysis.
Temporal Output Discrepancy for Loss Estimation-based Active Learning
Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this paper we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss.
Deep Active Learning with Noise Stability
We provide theoretical analyses by leveraging the small Gaussian noise theory and demonstrate that our method favors a subset with large and diverse gradients.
Towards Inadequately Pre-trained Models in Transfer Learning
Based on the contradictory phenomenon between FE and FT that better feature extractor fails to be fine-tuned better accordingly, we conduct comprehensive analyses on features before softmax layer to provide insightful explanations.
Boosting Active Learning via Improving Test Performance
In this work, we explore such an impact by theoretically proving that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss, resulting in better test performance.
Semi-Supervised Active Learning with Temporal Output Discrepancy
To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset.
An Evaluation of novel method of Ill-Posed Problem for the Black-Scholes Equation solution
It was proposed by Klibanov a new empirical mathematical method to work with the Black-Scholes equation.
Numerical Analysis Numerical Analysis 35R30, 65K05, 35R25, 65M30 G.1.8; G.1.6
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy
In this paper, comprehensive experimental studies of implementing state-of-the-art CNNs for the detection and classification of DR are conducted in order to determine the top performing classifiers for the task.
Parameter-Free Style Projection for Arbitrary Style Transfer
This paper further presents a real-time feed-forward model to leverage Style Projection for arbitrary image style transfer, which includes a regularization term for matching the semantics between input contents and stylized outputs.
Instance-based Deep Transfer Learning
It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain.
Data Dropout: Optimizing Training Data for Convolutional Neural Networks
In this paper, we demonstrate that deep learning models such as convolutional neural networks may not favor all training samples, and generalization accuracy can be further improved by dropping those unfavorable samples.
Imbalanced Malware Images Classification: a CNN based Approach
To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final layer of deep CNNs.
Dilated Deep Residual Network for Image Denoising
It has been proven that the expansion of receptive field can boost the CNN performance in image classification, and we further demonstrate that it can also lead to competitive performance for denoising problem.
An ELU Network with Total Variation for Image Denoising
In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses exponential linear unit (ELU) as the activation function.
