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Found 23 papers, 11 papers with code
AnimateZoo: Zero-shot Video Generation of Cross-Species Animation via Subject Alignment
In this paper, we present AnimateZoo, a zero-shot diffusion-based video generator to address this challenging cross-species animation issue, aiming to accurately produce animal animations while preserving the background.
Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima
This strategy can implicitly enhance the model's robustness during the optimization process, mitigating instability risks arising from multimodal information inputs.
Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus is suitable for LoT study.
ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection
Besides, we also observe the theoretical benefits of the LSC coefficient scaling of UNet in the stableness of hidden features and gradient and also robustness.
Understanding Self-attention Mechanism via Dynamical System Perspective
The self-attention mechanism (SAM) is widely used in various fields of artificial intelligence and has successfully boosted the performance of different models.
LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem
In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i. e., the ability to recognize and focus on key pixel regions in an image.
SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts.
ASR: Attention-alike Structural Re-parameterization
This technique enables the mitigation of the extra costs for performance improvement during training, such as parameter size and inference time, through these transformations during inference, and therefore SRP has great potential for industrial and practical applications.
On Robust Numerical Solver for ODE via Self-Attention Mechanism
With the development of deep learning techniques, AI-enhanced numerical solvers are expected to become a new paradigm for solving differential equations due to their versatility and effectiveness in alleviating the accuracy-speed trade-off in traditional numerical solvers.
Deepening Neural Networks Implicitly and Locally via Recurrent Attention Strategy
More and more empirical and theoretical evidence shows that deepening neural networks can effectively improve their performance under suitable training settings.
A Generic Shared Attention Mechanism for Various Backbone Neural Networks
The self-attention mechanism has emerged as a critical component for improving the performance of various backbone neural networks.
Causal Inference for Chatting Handoff
However, most existing methods mainly focus on the dialogue context or assist with global satisfaction prediction based on multi-task learning, which ignore the grounded relationships among the causal variables, like the user state and labor cost.
On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver
The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines.
The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network
Recently many plug-and-play self-attention modules (SAMs) are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs).
Stiffness-aware neural network for learning Hamiltonian systems
We propose stiffness-aware neural network (SANN), a new method for learning Hamiltonian dynamical systems from data.
AlterSGD: Finding Flat Minima for Continual Learning by Alternative Training
Deep neural networks suffer from catastrophic forgetting when learning multiple knowledge sequentially, and a growing number of approaches have been proposed to mitigate this problem.
Blending Pruning Criteria for Convolutional Neural Networks
One filter could be important according to a certain criterion, while it is unnecessary according to another one, which indicates that each criterion is only a partial view of the comprehensive "importance".
Rethinking the Pruning Criteria for Convolutional Neural Network
Channel pruning is a popular technique for compressing convolutional neural networks (CNNs), and various pruning criteria have been proposed to remove the redundant filters of CNNs.
Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp
Although deep reinforcement learning (RL) has been successfully applied to a variety of robotic control tasks, it's still challenging to apply it to real-world tasks, due to the poor sample efficiency.
Efficient Attention Network: Accelerate Attention by Searching Where to Plug
Recently, many plug-and-play self-attention modules are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs).
Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel Pruning
Channel pruning is a popular technique for compressing convolutional neural networks (CNNs), where various pruning criteria have been proposed to remove the redundant filters.
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch Noise
Batch Normalization (BN)(Ioffe and Szegedy 2015) normalizes the features of an input image via statistics of a batch of images and hence BN will bring the noise to the gradient of the training loss.
DIANet: Dense-and-Implicit Attention Network
Attention networks have successfully boosted the performance in various vision problems.
Ranked #139 on
Image Classification
on CIFAR-100
(using extra training data)
