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Found 12 papers, 1 papers with code
Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis
As such, there has been a recent focus on unsupervised learning approaches to mitigate the data annotation issue; however, current approaches in literature have limited performance compared to supervised learning approaches as well as limited applicability for adoption in new environments.
Fairest of Them All: Establishing a Strong Baseline for Cross-Domain Person ReID
There has been recent interest in tackling this challenge using cross-domain approaches, which leverages data from source domains that are different than the target domain.
Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning
In this study, we propose the leveraging of interpretability for tasks beyond purely the purpose of explainability.
SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells
Conclusion: The SISC radiomic sequencer is able to achieve state-of-the-art results in lung cancer prediction, and also offers prediction interpretability in the form of critical response maps.
Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy
Conclusion: We demonstrate the effectiveness and utility of the proposed CLEAR-DR system of enhancing the interpretability of diagnostic grading results for the application of diabetic retinopathy grading.
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction
However, current deep learning algorithms have been criticized as uninterpretable "black-boxes" which cannot explain their decision making processes.
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input.
Understanding Anatomy Classification Through Attentive Response Maps
One of the main challenges for broad adoption of deep learning based models such as convolutional neural networks (CNN), is the lack of understanding of their decisions.
Discovery Radiomics via StochasticNet Sequencers for Cancer Detection
In this study, we introduce a novel discovery radiomics framework where we directly discover custom radiomic features from the wealth of available medical imaging data.
Discovery Radiomics for Pathologically-Proven Computed Tomography Lung Cancer Prediction
In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics for lung cancer prediction using CT imaging data.
Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection
In this study, we propose a novel \textit{discovery radiomics} framework for generating custom radiomic sequences tailored for prostate cancer detection.
Recipe recognition with large multimodal food dataset
We present deep experiments of recipe recognition on our dataset using visual, textual information and fusion.
