69 research outputs found
LW-ISP: A Lightweight Model with ISP and Deep Learning
The deep learning (DL)-based methods of low-level tasks have many advantages
over the traditional camera in terms of hardware prospects, error accumulation
and imaging effects. Recently, the application of deep learning to replace the
image signal processing (ISP) pipeline has appeared one after another; however,
there is still a long way to go towards real landing. In this paper, we show
the possibility of learning-based method to achieve real-time high-performance
processing in the ISP pipeline. We propose LW-ISP, a novel architecture
designed to implicitly learn the image mapping from RAW data to RGB image.
Based on U-Net architecture, we propose the fine-grained attention module and a
plug-and-play upsampling block suitable for low-level tasks. In particular, we
design a heterogeneous distillation algorithm to distill the implicit features
and reconstruction information of the clean image, so as to guide the learning
of the student model. Our experiments demonstrate that LW-ISP has achieved a
0.38 dB improvement in PSNR compared to the previous best method, while the
model parameters and calculation have been reduced by 23 times and 81 times.
The inference efficiency has been accelerated by at least 15 times. Without
bells and whistles, LW-ISP has achieved quite competitive results in ISP
subtasks including image denoising and enhancement.Comment: 16 PAGES, ACCEPTED AS A CONFERENCE PAPER AT: BMVC 202
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
Convolutional neural networks have been widely deployed in various
application scenarios. In order to extend the applications' boundaries to some
accuracy-crucial domains, researchers have been investigating approaches to
boost accuracy through either deeper or wider network structures, which brings
with them the exponential increment of the computational and storage cost,
delaying the responding time. In this paper, we propose a general training
framework named self distillation, which notably enhances the performance
(accuracy) of convolutional neural networks through shrinking the size of the
network rather than aggrandizing it. Different from traditional knowledge
distillation - a knowledge transformation methodology among networks, which
forces student neural networks to approximate the softmax layer outputs of
pre-trained teacher neural networks, the proposed self distillation framework
distills knowledge within network itself. The networks are firstly divided into
several sections. Then the knowledge in the deeper portion of the networks is
squeezed into the shallow ones. Experiments further prove the generalization of
the proposed self distillation framework: enhancement of accuracy at average
level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as
maximum. In addition, it can also provide flexibility of depth-wise scalable
inference on resource-limited edge devices.Our codes will be released on github
soon.Comment: 10page
Recent advances in engineering characteristics of near-fault ground motions and seismic effects of building structures
Severe damages of civil infrastructures under near-fault ground motions have impelled the community of earthquake engineering to pay intensive attention and investigation to their engineering characteristics and structural seismic effects. This paper reviews the recent research advances of authors in the engineering characteristics of near-fault ground motions and seismic responses and base-isolated performance analysis of building structures. Firstly, two non-structure-specific intensity measures, such as improved effective peak acceleration and velocity (IEPA, IEPV) were proposed. Two frequency content parameters were also suggested, namely the mean period of Hilbert marginal spectrum Tmh, and coefficient of variance of dominant instantaneous frequency of Hilbert spectrum Hcov which reflects the frequency nonstationary degree of ground motions. Meanwhile, a new stochastic model to synthesize near-fault impulsive ground motions with the feature of the strongest pulse was established. Then, the chaotic and fractal/multifractal characteristics of strong earthquake ground motions were analyzed deeply to explore their complexity from a novel perspective of nonlinear dynamics, and the inherent relation between fractal dimensions and period parameters of near-fault motions was exposed. Moreover, the mechanism of interstory deformation of tall building was illustrated based on engineering properties of pulse-like ground motions and generalized drift spectral analysis. Finally, the influence of ground motion properties on the seismic responses and performance of tall structures and base isolated buildings was revealed
Contrastive Deep Supervision
The success of deep learning is usually accompanied by the growth in neural
network depth. However, the traditional training method only supervises the
neural network at its last layer and propagates the supervision layer-by-layer,
which leads to hardship in optimizing the intermediate layers. Recently, deep
supervision has been proposed to add auxiliary classifiers to the intermediate
layers of deep neural networks. By optimizing these auxiliary classifiers with
the supervised task loss, the supervision can be applied to the shallow layers
directly. However, deep supervision conflicts with the well-known observation
that the shallow layers learn low-level features instead of task-biased
high-level semantic features. To address this issue, this paper proposes a
novel training framework named Contrastive Deep Supervision, which supervises
the intermediate layers with augmentation-based contrastive learning.
Experimental results on nine popular datasets with eleven models demonstrate
its effects on general image classification, fine-grained image classification
and object detection in supervised learning, semi-supervised learning and
knowledge distillation. Codes have been released in Github.Comment: Accepted in ECCV202
In silico discovery of transcription regulatory elements in Plasmodium falciparum
<p>Abstract</p> <p>Background</p> <p>With the sequence of the <it>Plasmodium falciparum </it>genome and several global mRNA and protein life cycle expression profiling projects now completed, elucidating the underlying networks of transcriptional control important for the progression of the parasite life cycle is highly pertinent to the development of new anti-malarials. To date, relatively little is known regarding the specific mechanisms the parasite employs to regulate gene expression at the mRNA level, with studies of the <it>P. falciparum </it>genome sequence having revealed few <it>cis</it>-regulatory elements and associated transcription factors. Although it is possible the parasite may evoke mechanisms of transcriptional control drastically different from those used by other eukaryotic organisms, the extreme AT-rich nature of <it>P. falciparum </it>intergenic regions (~90% AT) presents significant challenges to <it>in silico cis</it>-regulatory element discovery.</p> <p>Results</p> <p>We have developed an algorithm called Gene Enrichment Motif Searching (GEMS) that uses a hypergeometric-based scoring function and a position-weight matrix optimization routine to identify with high-confidence regulatory elements in the nucleotide-biased and repeat sequence-rich <it>P. falciparum </it>genome. When applied to promoter regions of genes contained within 21 co-expression gene clusters generated from <it>P. falciparum </it>life cycle microarray data using the semi-supervised clustering algorithm Ontology-based Pattern Identification, GEMS identified 34 putative <it>cis</it>-regulatory elements associated with a variety of parasite processes including sexual development, cell invasion, antigenic variation and protein biosynthesis. Among these candidates were novel motifs, as well as many of the elements for which biological experimental evidence already exists in the <it>Plasmodium </it>literature. To provide evidence for the biological relevance of a cell invasion-related element predicted by GEMS, reporter gene and electrophoretic mobility shift assays were conducted.</p> <p>Conclusion</p> <p>This GEMS analysis demonstrates that <it>in silico </it>regulatory element discovery can be successfully applied to challenging repeat-sequence-rich, base-biased genomes such as that of <it>P. falciparum</it>. The fact that regulatory elements were predicted from a diverse range of functional gene clusters supports the hypothesis that <it>cis</it>-regulatory elements play a role in the transcriptional control of many <it>P. falciparum </it>biological processes. The putative regulatory elements described represent promising candidates for future biological investigation into the underlying transcriptional control mechanisms of gene regulation in malaria parasites.</p
When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks
In-context learning (ICL) has become the default method for using large
language models (LLMs), making the exploration of its limitations and
understanding the underlying causes crucial. In this paper, we find that ICL
falls short of handling specification-heavy tasks, which are tasks with
complicated and extensive task specifications, requiring several hours for
ordinary humans to master, such as traditional information extraction tasks.
The performance of ICL on these tasks mostly cannot reach half of the
state-of-the-art results. To explore the reasons behind this failure, we
conduct comprehensive experiments on 18 specification-heavy tasks with various
LLMs and identify three primary reasons: inability to specifically understand
context, misalignment in task schema comprehension with humans, and inadequate
long-text understanding ability. Furthermore, we demonstrate that through
fine-tuning, LLMs can achieve decent performance on these tasks, indicating
that the failure of ICL is not an inherent flaw of LLMs, but rather a drawback
of existing alignment methods that renders LLMs incapable of handling
complicated specification-heavy tasks via ICL. To substantiate this, we perform
dedicated instruction tuning on LLMs for these tasks and observe a notable
improvement. We hope the analyses in this paper could facilitate advancements
in alignment methods enabling LLMs to meet more sophisticated human demands.Comment: Under revie
Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction
The robustness to distribution changes ensures that NLP models can be
successfully applied in the realistic world, especially for information
extraction tasks. However, most prior evaluation benchmarks have been devoted
to validating pairwise matching correctness, ignoring the crucial measurement
of robustness. In this paper, we present the first benchmark that simulates the
evaluation of open information extraction models in the real world, where the
syntactic and expressive distributions under the same knowledge meaning may
drift variously. We design and annotate a large-scale testbed in which each
example is a knowledge-invariant clique that consists of sentences with
structured knowledge of the same meaning but with different syntactic and
expressive forms. By further elaborating the robustness metric, a model is
judged to be robust if its performance is consistently accurate on the overall
cliques. We perform experiments on typical models published in the last decade
as well as a popular large language model, the results show that the existing
successful models exhibit a frustrating degradation, with a maximum drop of
23.43 F1 score. Our resources and code are available at
https://github.com/qijimrc/ROBUST.Comment: Accepted by EMNLP 2023 Main Conferenc
MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation
Understanding events in texts is a core objective of natural language
understanding, which requires detecting event occurrences, extracting event
arguments, and analyzing inter-event relationships. However, due to the
annotation challenges brought by task complexity, a large-scale dataset
covering the full process of event understanding has long been absent. In this
paper, we introduce MAVEN-Arg, which augments MAVEN datasets with event
argument annotations, making the first all-in-one dataset supporting event
detection, event argument extraction (EAE), and event relation extraction. As
an EAE benchmark, MAVEN-Arg offers three main advantages: (1) a comprehensive
schema covering 162 event types and 612 argument roles, all with expert-written
definitions and examples; (2) a large data scale, containing 98,591 events and
290,613 arguments obtained with laborious human annotation; (3) the exhaustive
annotation supporting all task variants of EAE, which annotates both entity and
non-entity event arguments in document level. Experiments indicate that
MAVEN-Arg is quite challenging for both fine-tuned EAE models and proprietary
large language models (LLMs). Furthermore, to demonstrate the benefits of an
all-in-one dataset, we preliminarily explore a potential application, future
event prediction, with LLMs. MAVEN-Arg and our code can be obtained from
https://github.com/THU-KEG/MAVEN-Argument.Comment: Working in progres
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