164 research outputs found
Double-pulse Model for the Study of Red-shifted Spectrum in Multi-frequency Raman Generation
Multi-frequency Raman Generation (MRG) is a promising technique for generating few-femtosecond to sub-femtosecond pulses with high energy conversion efficiency. During transient MRG experiments coupled with two chirped pulses, when the instantaneous frequency separation between the pump and Stokes pulses is red-detuned from resonance, the individual Raman orders become a double-peak structure. These secondary peaks were only observed on the red side of the main Raman peaks.
A double pulse model (DPM) is used to describe the phenomenon of the Raman order: the Raman pulse and the red-shifted pulse. Comparison of FROG measured spectrograms with simulation using the DPM achieved lower errors compared to the Frequency Resolved Optical Gating (FROG) standard iterative program results. From the experimental results under various conditions of instantaneous frequency separations and input energies, the simulation suggested that the Raman pulse remains
similar to the pump pulses, while the red-shifted pulse exhibits extra higher-order phase due to the intensity-dependent two-photon Stark shift during Raman scattering. The 4-wave mixing is more dominant in blue shifted case. DPM simulation results show that this theory of two-photon Rabi frequency shifts matches the experimental results
Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis
Diffusion models (DMs) have recently gained attention with state-of-the-art
performance in text-to-image synthesis. Abiding by the tradition in deep
learning, DMs are trained and evaluated on the images with fixed sizes.
However, users are demanding for various images with specific sizes and various
aspect ratio. This paper focuses on adapting text-to-image diffusion models to
handle such variety while maintaining visual fidelity. First we observe that,
during the synthesis, lower resolution images suffer from incomplete object
portrayal, while higher resolution images exhibit repetitively disordered
presentation. Next, we establish a statistical relationship indicating that
attention entropy changes with token quantity, suggesting that models aggregate
spatial information in proportion to image resolution. The subsequent
interpretation on our observations is that objects are incompletely depicted
due to limited spatial information for low resolutions, while repetitively
disorganized presentation arises from redundant spatial information for high
resolutions. From this perspective, we propose a scaling factor to alleviate
the change of attention entropy and mitigate the defective pattern observed.
Extensive experimental results validate the efficacy of the proposed scaling
factor, enabling models to achieve better visual effects, image quality, and
text alignment. Notably, these improvements are achieved without additional
training or fine-tuning techniques.Comment: Accepted by NeurIPS 2023. 23 pages, 13 figure
DANAA: Towards transferable attacks with double adversarial neuron attribution
While deep neural networks have excellent results in many fields, they are
susceptible to interference from attacking samples resulting in erroneous
judgments. Feature-level attacks are one of the effective attack types, which
targets the learnt features in the hidden layers to improve its transferability
across different models. Yet it is observed that the transferability has been
largely impacted by the neuron importance estimation results. In this paper, a
double adversarial neuron attribution attack method, termed `DANAA', is
proposed to obtain more accurate feature importance estimation. In our method,
the model outputs are attributed to the middle layer based on an adversarial
non-linear path. The goal is to measure the weight of individual neurons and
retain the features that are more important towards transferability. We have
conducted extensive experiments on the benchmark datasets to demonstrate the
state-of-the-art performance of our method. Our code is available at:
https://github.com/Davidjinzb/DANAAComment: Accepted by 19th International Conference on Advanced Data Mining and
Applications. (ADMA 2023
PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems
Processing-in-memory (PIM), an increasingly studied neuromorphic hardware,
promises orders of energy and throughput improvements for deep learning
inference. Leveraging the massively parallel and efficient analog computing
inside memories, PIM circumvents the bottlenecks of data movements in
conventional digital hardware. However, an extra quantization step (i.e. PIM
quantization), typically with limited resolution due to hardware constraints,
is required to convert the analog computing results into digital domain.
Meanwhile, non-ideal effects extensively exist in PIM quantization because of
the imperfect analog-to-digital interface, which further compromises the
inference accuracy.
In this paper, we propose a method for training quantized networks to
incorporate PIM quantization, which is ubiquitous to all PIM systems.
Specifically, we propose a PIM quantization aware training (PIM-QAT) algorithm,
and introduce rescaling techniques during backward and forward propagation by
analyzing the training dynamics to facilitate training convergence. We also
propose two techniques, namely batch normalization (BN) calibration and
adjusted precision training, to suppress the adverse effects of non-ideal
linearity and stochastic thermal noise involved in real PIM chips. Our method
is validated on three mainstream PIM decomposition schemes, and physically on a
prototype chip. Comparing with directly deploying conventionally trained
quantized model on PIM systems, which does not take into account this extra
quantization step and thus fails, our method provides significant improvement.
It also achieves comparable inference accuracy on PIM systems as that of
conventionally quantized models on digital hardware, across CIFAR10 and
CIFAR100 datasets using various network depths for the most popular network
topology.Comment: 25 pages, 12 figures, 8 table
Learning Accurate Entropy Model with Global Reference for Image Compression
In recent deep image compression neural networks, the entropy model plays a
critical role in estimating the prior distribution of deep image encodings.
Existing methods combine hyperprior with local context in the entropy
estimation function. This greatly limits their performance due to the absence
of a global vision. In this work, we propose a novel Global Reference Model for
image compression to effectively leverage both the local and the global context
information, leading to an enhanced compression rate. The proposed method scans
decoded latents and then finds the most relevant latent to assist the
distribution estimating of the current latent. A by-product of this work is the
innovation of a mean-shifting GDN module that further improves the performance.
Experimental results demonstrate that the proposed model outperforms the
rate-distortion performance of most of the state-of-the-art methods in the
industry
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