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Found 12 papers, 1 papers with code
What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability
Classifier calibration has received recent attention from the machine learning community due both to its practical utility in facilitating decision making, as well as the observation that modern neural network classifiers are poorly calibrated.
Measuring AI Systems Beyond Accuracy
Current test and evaluation (T&E) methods for assessing machine learning (ML) system performance often rely on incomplete metrics.
Factor Analysis on Citation, Using a Combined Latent and Logistic Regression Model
We propose a combined model, which integrates the latent factor model and the logistic regression model, for the citation network.
xBD: A Dataset for Assessing Building Damage from Satellite Imagery
xBD is the largest building damage assessment dataset to date, containing 850, 736 building annotations across 45, 362 km\textsuperscript{2} of imagery.
Ranked #5 on
2D Semantic Segmentation
on xBD
Constrained Generative Adversarial Networks for Interactive Image Generation
In our experiments, we show that our GAN framework is able to generate images that are of comparable quality to equivalent unsupervised GANs while satisfying a large number of the constraints provided by users, effectively changing a GAN into one that allows users interactive control over image generation without sacrificing image quality.
Exploiting Class Learnability in Noisy Data
To that end, we develop an online algorithm that works in conjunction with classifier and training algorithm, iteratively selecting training data for the classifier based on how well it appears to generalize on each class.
Generating Triples with Adversarial Networks for Scene Graph Construction
Driven by successes in deep learning, computer vision research has begun to move beyond object detection and image classification to more sophisticated tasks like image captioning or visual question answering.
Clickstream analysis for crowd-based object segmentation with confidence
Using a total of 29, 000 crowd annotations performed on publicly available data of different object classes, we show that (1) our method is highly accurate in estimating the segmentation quality based on clickstream data, (2) outperforms state-of-the-art methods for merging multiple annotations.
Active Perceptual Similarity Modeling with Auxiliary Information
A common way to learn such a model is from relative comparisons in the form of triplets: responses to queries of the form "Is object a more similar to b than it is to c?".
Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels
Finally, we perform qualitative assessments on the metrics learned by CAMEL and show that they identify and clearly articulate important factors in how the model performs inference.
Efficient Online Relative Comparison Kernel Learning
Learning a kernel matrix from relative comparison human feedback is an important problem with applications in collaborative filtering, object retrieval, and search.
Relative Comparison Kernel Learning with Auxiliary Kernels
In this work, we explore methods for aiding the process of learning a kernel with the help of auxiliary kernels built from more easily extractable information regarding the relationships among objects.
