250 research outputs found
Latent Space Energy-based Model for Fine-grained Open Set Recognition
Fine-grained open-set recognition (FineOSR) aims to recognize images
belonging to classes with subtle appearance differences while rejecting images
of unknown classes. A recent trend in OSR shows the benefit of generative
models to discriminative unknown detection. As a type of generative model,
energy-based models (EBM) are the potential for hybrid modeling of generative
and discriminative tasks. However, most existing EBMs suffer from density
estimation in high-dimensional space, which is critical to recognizing images
from fine-grained classes. In this paper, we explore the low-dimensional latent
space with energy-based prior distribution for OSR in a fine-grained visual
world. Specifically, based on the latent space EBM, we propose an
attribute-aware information bottleneck (AIB), a residual attribute feature
aggregation (RAFA) module, and an uncertainty-based virtual outlier synthesis
(UVOS) module to improve the expressivity, granularity, and density of the
samples in fine-grained classes, respectively. Our method is flexible to take
advantage of recent vision transformers for powerful visual classification and
generation. The method is validated on both fine-grained and general visual
classification datasets while preserving the capability of generating
photo-realistic fake images with high resolution
Matrix manipulations via unitary transformations and ancilla-state measurements
We propose protocols for calculating inner product, matrix addition and
matrix multiplication based on multiqubit Toffoli-type and the simplest
one-qubit operations and employ ancilla measurements to remove all garbage of
calculations. The depth (runtime) of the addition protocol is and that
of other protocols logarithmically increases with the dimensionality of the
considered matrices.Comment: 5 pages, 1 figur
Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule
As a modern ensemble technique, Deep Forest (DF) employs a cascading
structure to construct deep models, providing stronger representational power
compared to traditional decision forests. However, its greedy multi-layer
learning procedure is prone to overfitting, limiting model effectiveness and
generalizability. This paper presents an optimized Deep Forest, featuring
learnable, layerwise data augmentation policy schedules. Specifically, We
introduce the Cut Mix for Tabular data (CMT) augmentation technique to mitigate
overfitting and develop a population-based search algorithm to tailor
augmentation intensity for each layer. Additionally, we propose to incorporate
outputs from intermediate layers into a checkpoint ensemble for more stable
performance. Experimental results show that our method sets new
state-of-the-art (SOTA) benchmarks in various tabular classification tasks,
outperforming shallow tree ensembles, deep forests, deep neural network, and
AutoML competitors. The learned policies also transfer effectively to Deep
Forest variants, underscoring its potential for enhancing non-differentiable
deep learning modules in tabular signal processing
Human Motion Generation: A Survey
Human motion generation aims to generate natural human pose sequences and
shows immense potential for real-world applications. Substantial progress has
been made recently in motion data collection technologies and generation
methods, laying the foundation for increasing interest in human motion
generation. Most research within this field focuses on generating human motions
based on conditional signals, such as text, audio, and scene contexts. While
significant advancements have been made in recent years, the task continues to
pose challenges due to the intricate nature of human motion and its implicit
relationship with conditional signals. In this survey, we present a
comprehensive literature review of human motion generation, which, to the best
of our knowledge, is the first of its kind in this field. We begin by
introducing the background of human motion and generative models, followed by
an examination of representative methods for three mainstream sub-tasks:
text-conditioned, audio-conditioned, and scene-conditioned human motion
generation. Additionally, we provide an overview of common datasets and
evaluation metrics. Lastly, we discuss open problems and outline potential
future research directions. We hope that this survey could provide the
community with a comprehensive glimpse of this rapidly evolving field and
inspire novel ideas that address the outstanding challenges.Comment: 20 pages, 5 figure
Optimization of gas-filled quartz capillary discharge waveguide for high-energy laser wakefield acceleration
A hydrogen-filled capillary discharge waveguide made of quartz is presented for high-energy laser wakefield acceleration (LWFA). The experimental parameters (discharge current and gas pressure) were optimized to mitigate ablation by a quantitative analysis of the ablation plasma density inside the hydrogen-filled quartz capillary. The ablation plasma density was obtained by combining a spectroscopic measurement method with a calibrated gas transducer. In order to obtain a controllable plasma density and mitigate the ablation as much as possible, the range of suitable parameters was investigated. The experimental results demonstrated that the ablation in the quartz capillary could be mitigated by increasing the gas pressure to similar to 7.5-14.7 Torr and decreasing the discharge current to similar to 70-100 A. These optimized parameters are promising for future high-energy LWFA experiments based on the quartz capillary discharge waveguide
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