相关论文: PHAT: PHoto-z Accuracy Testing
Image classifiers are information-discarding machines, by design. Yet, how these models discard information remains mysterious. We hypothesize that one way for image classifiers to reach high accuracy is to first zoom to the most…
Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this…
We present a combined galaxy-quasar approach to template-fitting photometric redshift techniques and show the method to be a powerful one. The code (ImpZ) is presented, developed and applied to two spectroscopic redshift catalogues, namely…
The technique of photometric redshifts has become essential for the exploitation of multi-band extragalactic surveys. While the requirements on photo-zs for the study of galaxy evolution mostly pertain to the precision and to the fraction…
Photo-z error is one of the major sources of systematics degrading the accuracy of weak lensing cosmological inferences. Zhang et al. (2010) proposed a self-calibration method combining galaxy-galaxy correlations and galaxy-shear…
The microlensing parallax campaign with the $Spitzer$ space telescope aims to measure masses and distances of microlensing events seen towards the Galactic bulge, with a focus on planetary microlensing events. The hope is to measure how the…
Automated Program Repair (APR) can reduce the time developers spend debugging, allowing them to focus on other aspects of software development. Automatically generated bug patches are typically validated through software testing. However,…
Test-time adaptation (TTA) is an effective approach to mitigate performance degradation of trained models when encountering input distribution shifts at test time. However, existing TTA methods often suffer significant performance drops…
We propose a novel Iterative Predictor-Critic Code Decoding framework for real-world image dehazing, abbreviated as IPC-Dehaze, which leverages the high-quality codebook prior encapsulated in a pre-trained VQGAN. Apart from previous…
Detecting out-of-distribution (OOD) instances is crucial for NLP models in practical applications. Although numerous OOD detection methods exist, most of them are empirical. Backed by theoretical analysis, this paper advocates for the…
Broadband photometry offers a time and cost effective method to reconstruct the continuum emission of celestial objects. Thus, photometric redshift estimation has supported the scientific exploitation of extragalactic multiwavelength…
Photo-acoustic tomography (PAT) aims to leverage the photo-acoustic coupling between optical absorption of light sources and ultrasound (US) emission to obtain high contrast reconstructions of optical parameters with the high resolution of…
Target tracking is a popular problem with many potential applications. There has been a lot of effort on improving the quality of the detection of targets using cameras through different techniques. In general, with higher computational…
The Shear TEsting Programme (STEP) is a collaborative project to improve the accuracy and reliability of weak lensing measurement, in preparation for the next generation of wide-field surveys. We review sixteen current and emerging shear…
Photo-z errors, especially catastrophic errors, are a major uncertainty for precision weak lensing cosmology. We find that the shear-(galaxy number) density and density-density cross correlation measurements between photo-z bins, available…
There exist a wide range of single number metrics for assessing performance of classification algorithms, including AUC and the F1-score (Wikipedia lists 17 such metrics, with 27 different names). In this article, I propose a new metric to…
With the advent of the next generation of astrophysics experiments, the volume of data available to researchers will be greater than ever. As these projects will significantly drive down statistical uncertainties in measurements, it is…
Error detection in relational databases is critical for maintaining data quality and is fundamental to tasks such as data cleaning and assessment. Current error detection studies mostly employ the multi-detector approach to handle…
Test sets are an integral part of evaluating models and gauging progress in object recognition, and more broadly in computer vision and AI. Existing test sets for object recognition, however, suffer from shortcomings such as bias towards…
Current out-of-distribution (OOD) detection methods typically assume balanced in-distribution (ID) data, while most real-world data follow a long-tailed distribution. Previous approaches to long-tailed OOD detection often involve balancing…