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Found 14 papers, 6 papers with code
Efficient Diffusion-Driven Corruption Editor for Test-Time Adaptation
To address this, we propose a novel TTA method by leveraging a latent diffusion model (LDM) based image editing model and fine-tuning it with our newly introduced corruption modeling scheme.
SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
We construct an augmentation graph in the feature space of the pretrained model using the neighbor relationships between target features and propose spectral neighborhood clustering to identify partitions in the prediction space.
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error.
Improving Diffusion-Based Generative Models via Approximated Optimal Transport
Our approach aims to approximate and integrate optimal transport into the training process, significantly enhancing the ability of diffusion models to estimate the denoiser outputs accurately.
Ranked #4 on
Image Generation
on CIFAR-10
Gradient Alignment with Prototype Feature for Fully Test-time Adaptation
In context of Test-time Adaptation(TTA), we propose a regularizer, dubbed Gradient Alignment with Prototype feature (GAP), which alleviates the inappropriate guidance from entropy minimization loss from misclassified pseudo label.
On mitigating stability-plasticity dilemma in CLIP-guided image morphing via geodesic distillation loss
Large-scale language-vision pre-training models, such as CLIP, have achieved remarkable text-guided image morphing results by leveraging several unconditional generative models.
Stein Latent Optimization for Generative Adversarial Networks
Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner.
PuVAE: A Variational Autoencoder to Purify Adversarial Examples
Deep neural networks are widely used and exhibit excellent performance in many areas.
Ranked #2 on
Adversarial Defense against FGSM Attack
on MNIST
HexaGAN: Generative Adversarial Nets for Real World Classification
We evaluate the classification performance (F1-score) of the proposed method with 20% missingness and confirm up to a 5% improvement in comparison with the performance of combinations of state-of-the-art methods.
Deep Trustworthy Knowledge Tracing
Knowledge tracing (KT), a key component of an intelligent tutoring system, is a machine learning technique that estimates the mastery level of a student based on his/her past performance.
Reinforcement Learning based Recommender System using Biclustering Technique
A recommender system aims to recommend items that a user is interested in among many items.
How Generative Adversarial Networks and Their Variants Work: An Overview
Generative Adversarial Networks (GAN) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution.
Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data
Electronic health records (EHRs) have contributed to the computerization of patient records and can thus be used not only for efficient and systematic medical services, but also for research on biomedical data science.
Polyphonic Music Generation with Sequence Generative Adversarial Networks
We propose an application of sequence generative adversarial networks (SeqGAN), which are generative adversarial networks for discrete sequence generation, for creating polyphonic musical sequences.
Sound Audio and Speech Processing
