215 research outputs found
Mode Conversion Behavior of Guided Wave in a Pipe Inspection System Based on a Long Waveguide
To make clear the mode conversion behavior of S0-mode lamb wave and SH0-plate wave converting to the longitudinal mode guided wave and torsional mode guided wave in a pipe, respectively, the experiments were performed based on a previous built pipe inspection system. The pipe was wound with an L-shaped plate or a T-shaped plate as the waveguide, and the S0-wave and SH0-wave were excited separately in the waveguide. To carry out the objective, a meander-line coil electromagnetic acoustic transducer (EMAT) for S0-wave and a periodic permanent magnet (PPM) EMAT for SH0-wave were developed and optimized. Then, several comparison experiments were conducted to compare the efficiency of mode conversion. Experimental results showed that the T(0,1) mode, L(0,1) mode, and L(0,2) mode guided waves can be successfully detected when converted from the S0-wave or SH0-wave with different shaped waveguides. It can also be inferred that the S0-wave has a better ability to convert to the T(0,1) mode, while the SH0-wave is easier to convert to the L(0,1) mode and L(0,2) mode, and the L-shaped waveguide has a better efficiency than T-shaped waveguide
T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation
Despite the stunning ability to generate high-quality images by recent
text-to-image models, current approaches often struggle to effectively compose
objects with different attributes and relationships into a complex and coherent
scene. We propose T2I-CompBench, a comprehensive benchmark for open-world
compositional text-to-image generation, consisting of 6,000 compositional text
prompts from 3 categories (attribute binding, object relationships, and complex
compositions) and 6 sub-categories (color binding, shape binding, texture
binding, spatial relationships, non-spatial relationships, and complex
compositions). We further propose several evaluation metrics specifically
designed to evaluate compositional text-to-image generation. We introduce a new
approach, Generative mOdel fine-tuning with Reward-driven Sample selection
(GORS), to boost the compositional text-to-image generation abilities of
pretrained text-to-image models. Extensive experiments and evaluations are
conducted to benchmark previous methods on T2I-CompBench, and to validate the
effectiveness of our proposed evaluation metrics and GORS approach. Project
page is available at https://karine-h.github.io/T2I-CompBench/.Comment: Project page: https://karine-h.github.io/T2I-CompBench
Improved OOD Generalization via Conditional Invariant Regularizer
Recently, generalization on out-of-distribution (OOD) data with correlation
shift has attracted great attention. The correlation shift is caused by the
spurious attributes that correlate to the class label, as the correlation
between them may vary in training and test data. For such a problem, we show
that given the class label, the conditionally independent models of spurious
attributes are OOD generalizable. Based on this, a metric Conditional Spurious
Variation (CSV) which controls OOD generalization error, is proposed to measure
such conditional independence. To improve the OOD generalization, we regularize
the training process with the proposed CSV. Under mild assumptions, our
training objective can be formulated as a nonconvex-concave mini-max problem.
An algorithm with provable convergence rate is proposed to solve the problem.
Extensive empirical results verify our algorithm's efficacy in improving OOD
generalization
New Interpretations of Normalization Methods in Deep Learning
In recent years, a variety of normalization methods have been proposed to
help train neural networks, such as batch normalization (BN), layer
normalization (LN), weight normalization (WN), group normalization (GN), etc.
However, mathematical tools to analyze all these normalization methods are
lacking. In this paper, we first propose a lemma to define some necessary
tools. Then, we use these tools to make a deep analysis on popular
normalization methods and obtain the following conclusions: 1) Most of the
normalization methods can be interpreted in a unified framework, namely
normalizing pre-activations or weights onto a sphere; 2) Since most of the
existing normalization methods are scaling invariant, we can conduct
optimization on a sphere with scaling symmetry removed, which can help
stabilize the training of network; 3) We prove that training with these
normalization methods can make the norm of weights increase, which could cause
adversarial vulnerability as it amplifies the attack. Finally, a series of
experiments are conducted to verify these claims.Comment: Accepted by AAAI 202
Training Energy-Based Models with Diffusion Contrastive Divergences
Energy-Based Models (EBMs) have been widely used for generative modeling.
Contrastive Divergence (CD), a prevailing training objective for EBMs, requires
sampling from the EBM with Markov Chain Monte Carlo methods (MCMCs), which
leads to an irreconcilable trade-off between the computational burden and the
validity of the CD. Running MCMCs till convergence is computationally
intensive. On the other hand, short-run MCMC brings in an extra non-negligible
parameter gradient term that is difficult to handle. In this paper, we provide
a general interpretation of CD, viewing it as a special instance of our
proposed Diffusion Contrastive Divergence (DCD) family. By replacing the
Langevin dynamic used in CD with other EBM-parameter-free diffusion processes,
we propose a more efficient divergence. We show that the proposed DCDs are both
more computationally efficient than the CD and are not limited to a
non-negligible gradient term. We conduct intensive experiments, including both
synthesis data modeling and high-dimensional image denoising and generation, to
show the advantages of the proposed DCDs. On the synthetic data learning and
image denoising experiments, our proposed DCD outperforms CD by a large margin.
In image generation experiments, the proposed DCD is capable of training an
energy-based model for generating the Celab-A dataset, which is
comparable to existing EBMs
Non-contacted Permanent Magnetic Absorbed Wall-climbing Robot for Ultrasonic Weld Inspection of Spherical Tank
A novel wall-climbing robot for the onsite weld inspecting of spherical tank with Time of Flight Diffraction (TOFD) method has been developed, in order to liberate operators from dangerous and heavy working environment. Patented non-contacted permanent magnetic absorbed technology was adopted to realize reliable and flexible all-position moving along the weld seam on the surface of spherical tank. To ensure stable coupling for TOFD test, a surface adaptive probe holder which can provide constant contact force, have been particularly designed. Equipped with a visual sensing based weld seam tracking unit and industrial PC station, the robot could perform automatic flaw detection remotely even in the darkness environment. Onsite ultrasonic weld inspections have been carried out on a 4000m3 spherical tank with 28mm in thickness. It is verified that the robot could accomplish tasks in any position and the acquired TOFD images satisfy the requirements of engineering evaluation
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
Due to the ease of training, ability to scale, and high sample quality,
diffusion models (DMs) have become the preferred option for generative
modeling, with numerous pre-trained models available for a wide variety of
datasets. Containing intricate information about data distributions,
pre-trained DMs are valuable assets for downstream applications. In this work,
we consider learning from pre-trained DMs and transferring their knowledge to
other generative models in a data-free fashion. Specifically, we propose a
general framework called Diff-Instruct to instruct the training of arbitrary
generative models as long as the generated samples are differentiable with
respect to the model parameters. Our proposed Diff-Instruct is built on a
rigorous mathematical foundation where the instruction process directly
corresponds to minimizing a novel divergence we call Integral Kullback-Leibler
(IKL) divergence. IKL is tailored for DMs by calculating the integral of the KL
divergence along a diffusion process, which we show to be more robust in
comparing distributions with misaligned supports. We also reveal non-trivial
connections of our method to existing works such as DreamFusion, and generative
adversarial training. To demonstrate the effectiveness and universality of
Diff-Instruct, we consider two scenarios: distilling pre-trained diffusion
models and refining existing GAN models. The experiments on distilling
pre-trained diffusion models show that Diff-Instruct results in
state-of-the-art single-step diffusion-based models. The experiments on
refining GAN models show that the Diff-Instruct can consistently improve the
pre-trained generators of GAN models across various settings
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