Search Results for author:
Found 14 papers, 12 papers with code
DD-Ranking: Rethinking the Evaluation of Dataset Distillation
In recent years, dataset distillation has provided a reliable solution for data compression, where models trained on the resulting smaller synthetic datasets achieve performance comparable to those trained on the original datasets.
Dynamic Vision Mamba
For block redundancy, we allow each image to select SSM blocks dynamically based on an empirical observation that the inference speed of Mamba-based vision models is largely affected by the number of SSM blocks.
Data-to-Model Distillation: Data-Efficient Learning Framework
In this paper, we propose a novel framework called Data-to-Model Distillation (D2M) to distill the real dataset's knowledge into the learnable parameters of a pre-trained generative model by aligning rich representations extracted from real and generated images.
Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios
Hopefully, more researchers will be inspired and encouraged to improve the practicality and efficacy of DD.
Prioritize Alignment in Dataset Distillation
To achieve this, existing methods use the agent model to extract information from the target dataset and embed it into the distilled dataset.
Sparse Refinement for Efficient High-Resolution Semantic Segmentation
Finally, it leverages a gated ensembler to apply these sparse refinements to the initial coarse predictions.
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).
