170 research outputs found
The M33 Synoptic Stellar Survey. II. Mira Variables
We present the discovery of 1847 Mira candidates in the Local Group galaxy
M33 using a novel semi-parametric periodogram technique coupled with a Random
Forest classifier. The algorithms were applied to ~2.4x10^5 I-band light curves
previously obtained by the M33 Synoptic Stellar Survey. We derive preliminary
Period-Luminosity relations at optical, near- & mid-infrared wavelengths and
compare them to the corresponding relations in the Large Magellanic Cloud.Comment: Includes small corrections to match the published versio
Task-Adaptive Negative Class Envision for Few-Shot Open-Set Recognition
Recent works seek to endow recognition systems with the ability to handle the
open world. Few shot learning aims for fast learning of new classes from
limited examples, while open-set recognition considers unknown negative class
from the open world. In this paper, we study the problem of few-shot open-set
recognition (FSOR), which learns a recognition system robust to queries from
new sources with few examples and from unknown open sources. To achieve that,
we mimic human capability of envisioning new concepts from prior knowledge, and
propose a novel task-adaptive negative class envision method (TANE) to model
the open world. Essentially we use an external memory to estimate a negative
class representation. Moreover, we introduce a novel conjugate episode training
strategy that strengthens the learning process. Extensive experiments on four
public benchmarks show that our approach significantly improves the
state-of-the-art performance on few-shot open-set recognition. Besides, we
extend our method to generalized few-shot open-set recognition (GFSOR), where
we also achieve performance gains on MiniImageNet
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