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Found 11 papers, 4 papers with code
BinaryDM: Towards Accurate Binarization of Diffusion Model
With the advancement of diffusion models (DMs) and the substantially increased computational requirements, quantization emerges as a practical solution to obtain compact and efficient low-bit DMs.
DB-LLM: Accurate Dual-Binarization for Efficient LLMs
Large language models (LLMs) have significantly advanced the field of natural language processing, while the expensive memory and computation consumption impede their practical deployment.
RobustMQ: Benchmarking Robustness of Quantized Models
Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources.
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
The channel-wise shifting aligns the center of each channel for removal of outlier asymmetry.
Annealing-Based Label-Transfer Learning for Open World Object Detection
To the best of our knowledge, this is the first OWOD work without manual unknown selection.
Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Prediction
The previous deep video compression approaches only use the single scale motion compensation strategy and rarely adopt the mode prediction technique from the traditional standards like H. 264/H. 265 for both motion and residual compression.
CoupleFace: Relation Matters for Face Recognition Distillation
In this work, we observe that mutual relation knowledge between samples is also important to improve the discriminative ability of the learned representation of the student model, and propose an effective face recognition distillation method called CoupleFace by additionally introducing the Mutual Relation Distillation (MRD) into existing distillation framework.
Unsupervised Learning of Accurate Siamese Tracking
As unlimited self-supervision signals can be obtained by tracking a video along a cycle in time, we investigate evolving a Siamese tracker by tracking videos forward-backward.
CBANet: Towards Complexity and Bitrate Adaptive Deep Image Compression using a Single Network
Furthermore, to achieve variable bitrate decoding with one single decoder, we propose a bitrate adaptive module to project the representation from a base bitrate to the expected representation at a target bitrate for transmission.
Multi-Dimensional Pruning: A Unified Framework for Model Compression
Specifically, in order to reduce the redundancy along the spatial/spatial-temporal dimension, we downsample the input tensor of a convolutional layer, in which the scaling factor for the downsampling operation is adaptively selected by our approach.
Channel Pruning Guided by Classification Loss and Feature Importance
To this end, we propose a new strategy to suppress the influence of unimportant features (i. e., the features will be removed at the next pruning stage).
