Search Results for author:
Found 22 papers, 17 papers with code
Ungeneralizable Examples
The training of contemporary deep learning models heavily relies on publicly available data, posing a risk of unauthorized access to online data and raising concerns about data privacy.
Distilled Datamodel with Reverse Gradient Matching
To investigate the impact of changes in training data on a pre-trained model, a common approach is leave-one-out retraining.
Mutual-modality Adversarial Attack with Semantic Perturbation
Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization
Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data.
Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate
Existing approaches either apply the holistic style of the style image in a global manner, or migrate local colors and textures of the style image to the content counterparts in a pre-defined way.
Partial Network Cloning
Unlike prior methods that update all or at least part of the parameters in the target network throughout the knowledge transfer process, PNC conducts partial parametric "cloning" from a source network and then injects the cloned module to the target, without modifying its parameters.
Slimmable Dataset Condensation
In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results.
Dataset Factorization for Condensation
In this paper, we study dataset distillation (DD), from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline.
Dataset Distillation via Factorization
In this paper, we study \xw{dataset distillation (DD)}, from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline.
Deep Model Reassembly
Given a collection of heterogeneous models pre-trained from distinct sources and with diverse architectures, the goal of DeRy, as its name implies, is to first dissect each model into distinctive building blocks, and then selectively reassemble the derived blocks to produce customized networks under both the hardware resource and performance constraints.
A Survey of Neural Trees
This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability.
Learning with Recoverable Forgetting
Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge.
DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
Prior approaches, despite the promising results, have relied on either estimating dense attention to compute per-point matching, which is limited to only coarse scales due to the quadratic memory cost, or fixing the number of correspondences to achieve linear complexity, which lacks flexibility.
Factorizing Knowledge in Neural Networks
The core idea of KF lies in the modularization and assemblability of knowledge: given a pretrained network model as input, KF aims to decompose it into several factor networks, each of which handles only a dedicated task and maintains task-specific knowledge factorized from the source network.
Spot-adaptive Knowledge Distillation
Knowledge distillation (KD) has become a well established paradigm for compressing deep neural networks.
Safe Distillation Box
In other words, all users may employ a model in SDB for inference, but only authorized users get access to KD from the model.
Online Knowledge Distillation for Efficient Pose Estimation
However, existing pose distillation works rely on a heavy pre-trained estimator to perform knowledge transfer and require a complex two-stage learning procedure.
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN
Then a dual generator is trained by taking the output from the former generator as input.
DEPARA: Deep Attribution Graph for Deep Knowledge Transferability
In this paper, we propose the DEeP Attribution gRAph (DEPARA) to investigate the transferability of knowledge learned from PR-DNNs.
Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers
Many well-trained Convolutional Neural Network(CNN) models have now been released online by developers for the sake of effortless reproducing.
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More
In this paper, we investigate a novel deep-model reusing task.
Neural Style Transfer: A Review
We first propose a taxonomy of current algorithms in the field of NST.
