Search Results for author:
Found 8 papers, 7 papers with code
ATOM: Attention Mixer for Efficient Dataset Distillation
To address these challenges in dataset distillation, we propose the ATtentiOn Mixer (ATOM) module to efficiently distill large datasets using a mixture of channel and spatial-wise attention in the feature matching process.
The Need for Speed: Pruning Transformers with One Recipe
We introduce the $\textbf{O}$ne-shot $\textbf{P}$runing $\textbf{T}$echnique for $\textbf{I}$nterchangeable $\textbf{N}$etworks ($\textbf{OPTIN}$) framework as a tool to increase the efficiency of pre-trained transformer architectures $\textit{without requiring re-training}$.
ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification
We validate the effectiveness of our framework through experimentation with datasets from the computer vision and medical imaging domains.
DataDAM: Efficient Dataset Distillation with Attention Matching
Emerging research on dataset distillation aims to reduce training costs by creating a small synthetic set that contains the information of a larger real dataset and ultimately achieves test accuracy equivalent to a model trained on the whole dataset.
End-to-End Supervised Multilabel Contrastive Learning
However, they fail to design an end-to-end training framework, leading to high computational complexity.
CFDP: Common Frequency Domain Pruning
In this paper, we introduce a novel end-to-end pipeline for model pruning via the frequency domain.
Computational Pathology: A Survey Review and The Way Forward
Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images.
CONetV2: Efficient Auto-Channel Size Optimization for CNNs
Neural Architecture Search (NAS) has been pivotal in finding optimal network configurations for Convolution Neural Networks (CNNs).
