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Found 6 papers, 3 papers with code
Toward Open-Set Face Recognition
In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity.
To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics
We propose an innovative method to formulate the issue of localizing manipulated regions in an image as a deep representation learning problem using the Information Bottleneck (IB), which has recently gained popularity as a framework for interpreting deep neural networks.
Automatic Open-World Reliability Assessment
However, during operations, we cannot directly assess accuracy as there are no labels.
A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels
Open-world learning is related to but also distinct from a multitude of other learning problems and this paper briefly analyzes the key differences between a wide range of problems including incremental learning, generalized novelty discovery, and generalized zero-shot learning.
Zillow Indoor Dataset: Annotated Floor Plans With 360deg Panoramas and 3D Room Layouts
We present Zillow Indoor Dataset (ZInD): A large indoor dataset with 71, 474 panoramas from 1, 524 real unfurnished homes.
Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions
Specifically, we demonstrate that running inference on the center crop of an image is not always the best as important discriminatory information may be cropped-off.
