Search Results for author:
Found 18 papers, 8 papers with code
Task-Oriented Diffusion Model Compression
As recent advancements in large-scale Text-to-Image (T2I) diffusion models have yielded remarkable high-quality image generation, diverse downstream Image-to-Image (I2I) applications have emerged.
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models
The substantial computational costs of diffusion models, especially due to the repeated denoising steps necessary for high-quality image generation, present a major obstacle to their widespread adoption.
Temporal Dynamic Quantization for Diffusion Models
The diffusion model has gained popularity in vision applications due to its remarkable generative performance and versatility.
OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
Large language models (LLMs) with hundreds of billions of parameters require powerful server-grade GPUs for inference, limiting their practical deployment.
BASQ: Branch-wise Activation-clipping Search Quantization for Sub-4-bit Neural Networks
In this paper, we propose Branch-wise Activation-clipping Search Quantization (BASQ), which is a novel quantization method for low-bit activation.
Symmetry Regularization and Saturating Nonlinearity for Robust Quantization
Robust quantization improves the tolerance of networks for various implementations, allowing reliable output in different bit-widths or fragmented low-precision arithmetic.
NIPQ: Noise proxy-based Integrated Pseudo-Quantization
Recently, pseudoquantization training has been proposed as an alternative approach to updating the learnable parameters using the pseudo-quantization noise instead of STE.
Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking
This paper presents a novel hybrid representation learning framework for streaming data, where an image frame in a video is modeled by an ensemble of two distinct deep neural networks; one is a low-bit quantized network and the other is a lightweight full-precision network.
INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold
Binary Neural Networks (BNNs) have emerged as a promising solution for reducing the memory footprint and compute costs of deep neural networks, but they suffer from quality degradation due to the lack of freedom as activations and weights are constrained to the binary values.
On the Overlooked Significance of Underutilized Contextual Features in Recent News Recommendation Models
Personalized news recommendation aims to provide attractive articles for readers by predicting their likelihood of clicking on a certain article.
Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation
Self-supervised monocular depth estimation has been widely studied, owing to its practical importance and recent promising improvements.
MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendation
Following the custom from language processing, most of these models rely on a simple positional embedding to exploit the sequential nature of the user's history.
PROFIT: A Novel Training Method for sub-4-bit MobileNet Models
In the ablation study of the 3-bit quantization of MobileNet-v3, our proposed method outperforms the state-of-the-art method by a large margin, 12. 86 % of top-1 accuracy.
Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss
A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image.
Precision Highway for Ultra Low-Precision Quantization
We also report that the proposed method significantly outperforms the existing method in the 2-bit quantization of an LSTM for language modeling.
Value-aware Quantization for Training and Inference of Neural Networks
We propose a novel value-aware quantization which applies aggressively reduced precision to the majority of data while separately handling a small amount of large data in high precision, which reduces total quantization errors under very low precision.
Weighted-Entropy-Based Quantization for Deep Neural Networks
Quantization is considered as one of the most effective methods to optimize the inference cost of neural network models for their deployment to mobile and embedded systems, which have tight resource constraints.
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications
Although the latest high-end smartphone has powerful CPU and GPU, running deeper convolutional neural networks (CNNs) for complex tasks such as ImageNet classification on mobile devices is challenging.
