192 research outputs found
Quantum circuit complexity of one-dimensional topological phases
Topological quantum states cannot be created from product states with local
quantum circuits of constant depth and are in this sense more entangled than
topologically trivial states, but how entangled are they? Here we quantify the
entanglement in one-dimensional topological states by showing that local
quantum circuits of linear depth are necessary to generate them from product
states. We establish this linear lower bound for both bosonic and fermionic
one-dimensional topological phases and use symmetric circuits for phases with
symmetry. We also show that the linear lower bound can be saturated by
explicitly constructing circuits generating these topological states. The same
results hold for local quantum circuits connecting topological states in
different phases.Comment: published versio
Out-of-time-ordered correlators in many-body localized systems
In many-body localized systems, propagation of information forms a light cone that grows logarithmically with time. However, local changes in energy or other conserved quantities typically spread only within a finite distance. Is it possible to detect the logarithmic light cone generated by a local perturbation from the response of a local operator at a later time? We numerically calculate various correlators in the random-field Heisenberg chain. While the equilibrium retarded correlator A(t = 0)B(t > 0) is not sensitive to the unbounded information propagation, the out-of-time-ordered correlator A(t = 0)B(t > 0)A(t = 0)B(t > 0) can detect the logarithmic light cone. We relate out-of-time-ordered correlators to the Lieb-Robinson bound in many-body localized systems, and show how to detect the logarithmic light cone with retarded correlators in specially designed states. Furthermore, we study the temperature dependence of the logarithmic light cone using out-of-time-ordered correlators
Towards Free Data Selection with General-Purpose Models
A desirable data selection algorithm can efficiently choose the most
informative samples to maximize the utility of limited annotation budgets.
However, current approaches, represented by active learning methods, typically
follow a cumbersome pipeline that iterates the time-consuming model training
and batch data selection repeatedly. In this paper, we challenge this status
quo by designing a distinct data selection pipeline that utilizes existing
general-purpose models to select data from various datasets with a single-pass
inference without the need for additional training or supervision. A novel free
data selection (FreeSel) method is proposed following this new pipeline.
Specifically, we define semantic patterns extracted from inter-mediate features
of the general-purpose model to capture subtle local information in each image.
We then enable the selection of all data samples in a single pass through
distance-based sampling at the fine-grained semantic pattern level. FreeSel
bypasses the heavy batch selection process, achieving a significant improvement
in efficiency and being 530x faster than existing active learning methods.
Extensive experiments verify the effectiveness of FreeSel on various computer
vision tasks. Our code is available at https://github.com/yichen928/FreeSel.Comment: accepted by NeurIPS 202
DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection
Recent years, human-object interaction (HOI) detection has achieved
impressive advances. However, conventional two-stage methods are usually slow
in inference. On the other hand, existing one-stage methods mainly focus on the
union regions of interactions, which introduce unnecessary visual information
as disturbances to HOI detection. To tackle the problems above, we propose a
novel one-stage HOI detection approach DIRV in this paper, based on a new
concept called interaction region for the HOI problem. Unlike previous methods,
our approach concentrates on the densely sampled interaction regions across
different scales for each human-object pair, so as to capture the subtle visual
features that is most essential to the interaction. Moreover, in order to
compensate for the detection flaws of a single interaction region, we introduce
a novel voting strategy that makes full use of those overlapped interaction
regions in place of conventional Non-Maximal Suppression (NMS). Extensive
experiments on two popular benchmarks: V-COCO and HICO-DET show that our
approach outperforms existing state-of-the-arts by a large margin with the
highest inference speed and lightest network architecture. We achieved 56.1 mAP
on V-COCO without addtional input. Our code is publicly available at:
https://github.com/MVIG-SJTU/DIRVComment: Paper is accepted. Code available at:
https://github.com/MVIG-SJTU/DIR
Learning to Purify Noisy Labels via Meta Soft Label Corrector
Recent deep neural networks (DNNs) can easily overfit to biased training data
with noisy labels. Label correction strategy is commonly used to alleviate this
issue by designing a method to identity suspected noisy labels and then correct
them. Current approaches to correcting corrupted labels usually need certain
pre-defined label correction rules or manually preset hyper-parameters. These
fixed settings make it hard to apply in practice since the accurate label
correction usually related with the concrete problem, training data and the
temporal information hidden in dynamic iterations of training process. To
address this issue, we propose a meta-learning model which could estimate soft
labels through meta-gradient descent step under the guidance of noise-free meta
data. By viewing the label correction procedure as a meta-process and using a
meta-learner to automatically correct labels, we could adaptively obtain
rectified soft labels iteratively according to current training problems
without manually preset hyper-parameters. Besides, our method is model-agnostic
and we can combine it with any other existing model with ease. Comprehensive
experiments substantiate the superiority of our method in both synthetic and
real-world problems with noisy labels compared with current SOTA label
correction strategies.Comment: 12 pages,6 figure
Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification
Classifying incomplete multi-view data is inevitable since arbitrary view
missing widely exists in real-world applications. Although great progress has
been achieved, existing incomplete multi-view methods are still difficult to
obtain a trustworthy prediction due to the relatively high uncertainty nature
of missing views. First, the missing view is of high uncertainty, and thus it
is not reasonable to provide a single deterministic imputation. Second, the
quality of the imputed data itself is of high uncertainty. To explore and
exploit the uncertainty, we propose an Uncertainty-induced Incomplete
Multi-View Data Classification (UIMC) model to classify the incomplete
multi-view data under a stable and reliable framework. We construct a
distribution and sample multiple times to characterize the uncertainty of
missing views, and adaptively utilize them according to the sampling quality.
Accordingly, the proposed method realizes more perceivable imputation and
controllable fusion. Specifically, we model each missing data with a
distribution conditioning on the available views and thus introducing
uncertainty. Then an evidence-based fusion strategy is employed to guarantee
the trustworthy integration of the imputed views. Extensive experiments are
conducted on multiple benchmark data sets and our method establishes a
state-of-the-art performance in terms of both performance and trustworthiness.Comment: CVP
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