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Found 22 papers, 10 papers with code
Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack
The growing demand for customized visual content has led to the rise of personalized text-to-image (T2I) diffusion models.
Multi-Class Textual-Inversion Secretly Yields a Semantic-Agnostic Classifier
However, in many realistic scenarios, we only have access to few samples and knowledge of the class names (e. g., when considering instances of classes).
Assessing Open-world Forgetting in Generative Image Model Customization
Our research presents the first comprehensive investigation into open-world forgetting in diffusion models, focusing on semantic and appearance drift of representations.
The Expanding Scope of the Stability Gap: Unveiling its Presence in Joint Incremental Learning of Homogeneous Tasks
In this paper, we show that the stability gap also occurs when applying joint incremental training of homogeneous tasks.
Continual Evidential Deep Learning for Out-of-Distribution Detection
Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions.
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM).
Transferring Unconditional to Conditional GANs with Hyper-Modulation
GANs have matured in recent years and are able to generate high-resolution, realistic images.
Class-Balanced Active Learning for Image Classification
Results on three datasets showed that the method is general (it can be combined with most existing active learning algorithms) and can be effectively applied to boost the performance of both informative and representative-based active learning methods.
Reducing Label Effort: Self-Supervised meets Active Learning
Our experiments reveal that self-training is remarkably more efficient than active learning at reducing the labeling effort, that for a low labeling budget, active learning offers no benefit to self-training, and finally that the combination of active learning and self-training is fruitful when the labeling budget is high.
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning
Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples.
Saliency for free: Saliency prediction as a side-effect of object recognition
Saliency is the perceptual capacity of our visual system to focus our attention (i. e. gaze) on relevant objects.
TransferI2I: Transfer Learning for Image-to-Image Translation from Small Datasets
In this paper, we propose a new transfer learning for I2I translation (TransferI2I).
Learning to Rank for Active Learning: A Listwise Approach
Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.).
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains
Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classification.
Generative Feature Replay For Class-Incremental Learning
To prevent forgetting, we combine generative feature replay in the classifier with feature distillation in the feature extractor.
Temporal Coherence for Active Learning in Videos
Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives.
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation
Additionally, we propose and analyse network distillation as a learning strategy to reduce the computational cost of the deep learning approach at test time.
Memory Replay GANs: Learning to Generate New Categories without Forgetting
In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion.
Memory Replay GANs: learning to generate images from new categories without forgetting
In particular, we investigate generative adversarial networks (GANs) in the task of learning new categories in a sequential fashion.
Saliency for Fine-grained Object Recognition in Domains with Scarce Training Data
Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition.
Transferring GANs: generating images from limited data
Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models.
Ranked #7 on
10-shot image generation
on Babies
Invertible Conditional GANs for image editing
Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions.
Ranked #4 on
Image-to-Image Translation
on RaFD
