79 research outputs found
An Ultraluminous Supersoft X-ray Source in M81: An Intermediate-Mass Black Hole?
Ultraluminous supersoft X-ray sources (ULSSS) exhibit supersoft spectra with
blackbody temperatures of 50-100 eV and bolometric luminosities above
erg s, and are possibly intermediate mass black holes (IMBHs) of
or massive white dwarfs that are progenitors of type Ia
supernovae. In this letter we report our optical studies of such a source in
M81, M81-ULS1, with HST archive observations. M81-ULS1 is identified with a
point-like object, the spectral energy distribution of which reveals a blue
component in addition to the companion of an AGB star. The blue component is
consistent with the power-law as expected from the geometrically-thin accretion
disk around an IMBH accretor, but inconsistent with the power-law as expected
from the X-ray irradiated flared accretion disk around a white dwarf accretor.
This result is strong evidence that M81-ULS1 is an IMBH instead of a white
dwarf.Comment: 12 pages, 1 table, 3 figure
What does a platypus look like? Generating customized prompts for zero-shot image classification
Open vocabulary models are a promising new paradigm for image classification.
Unlike traditional classification models, open vocabulary models classify among
any arbitrary set of categories specified with natural language during
inference. This natural language, called "prompts", typically consists of a set
of hand-written templates (e.g., "a photo of a {}") which are completed with
each of the category names. This work introduces a simple method to generate
higher accuracy prompts, without using explicit knowledge of the image domain
and with far fewer hand-constructed sentences. To achieve this, we combine open
vocabulary models with large language models (LLMs) to create Customized
Prompts via Language models (CuPL, pronounced "couple"). In particular, we
leverage the knowledge contained in LLMs in order to generate many descriptive
sentences that are customized for each object category. We find that this
straightforward and general approach improves accuracy on a range of zero-shot
image classification benchmarks, including over one percentage point gain on
ImageNet. Finally, this method requires no additional training and remains
completely zero-shot. Code is available at https://github.com/sarahpratt/CuPL
Extremely Simple Activation Shaping for Out-of-Distribution Detection
The separation between training and deployment of machine learning models
implies that not all scenarios encountered in deployment can be anticipated
during training, and therefore relying solely on advancements in training has
its limits. Out-of-distribution (OOD) detection is an important area that
stress-tests a model's ability to handle unseen situations: Do models know when
they don't know? Existing OOD detection methods either incur extra training
steps, additional data or make nontrivial modifications to the trained network.
In contrast, in this work, we propose an extremely simple, post-hoc, on-the-fly
activation shaping method, ASH, where a large portion (e.g. 90%) of a sample's
activation at a late layer is removed, and the rest (e.g. 10%) simplified or
lightly adjusted. The shaping is applied at inference time, and does not
require any statistics calculated from training data. Experiments show that
such a simple treatment enhances in-distribution and out-of-distribution sample
distinction so as to allow state-of-the-art OOD detection on ImageNet, and does
not noticeably deteriorate the in-distribution accuracy. We release alongside
the paper two calls for explanation and validation, believing the collective
power to further validate and understand the discovery. Calls, video and code
can be found at: https://andrijazz.github.io/ashComment: Preprint. 22 pages (14 main + appendix), 7 figure
Natural Adversarial Objects
Although state-of-the-art object detection methods have shown compelling
performance, models often are not robust to adversarial attacks and
out-of-distribution data. We introduce a new dataset, Natural Adversarial
Objects (NAO), to evaluate the robustness of object detection models. NAO
contains 7,934 images and 9,943 objects that are unmodified and representative
of real-world scenarios, but cause state-of-the-art detection models to
misclassify with high confidence. The mean average precision (mAP) of
EfficientDet-D7 drops 74.5% when evaluated on NAO compared to the standard
MSCOCO validation set.
Moreover, by comparing a variety of object detection architectures, we find
that better performance on MSCOCO validation set does not necessarily translate
to better performance on NAO, suggesting that robustness cannot be simply
achieved by training a more accurate model.
We further investigate why examples in NAO are difficult to detect and
classify. Experiments of shuffling image patches reveal that models are overly
sensitive to local texture. Additionally, using integrated gradients and
background replacement, we find that the detection model is reliant on pixel
information within the bounding box, and insensitive to the background context
when predicting class labels. NAO can be downloaded at
https://drive.google.com/drive/folders/15P8sOWoJku6SSEiHLEts86ORfytGezi8
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