Search Results for author:
Found 7 papers, 4 papers with code
Privacy-Preserving Face Recognition Using Trainable Feature Subtraction
Recognizable identity features within the image are encouraged by co-training a recognition model on its high-dimensional feature representation.
Generalized Category Discovery in Semantic Segmentation
This paper explores a novel setting called Generalized Category Discovery in Semantic Segmentation (GCDSS), aiming to segment unlabeled images given prior knowledge from a labeled set of base classes.
Proximity-Informed Calibration for Deep Neural Networks
We examine the problem over 504 pretrained ImageNet models and observe that: 1) Proximity bias exists across a wide variety of model architectures and sizes; 2) Transformer-based models are relatively more susceptible to proximity bias than CNN-based models; 3) Proximity bias persists even after performing popular calibration algorithms like temperature scaling; 4) Models tend to overfit more heavily on low proximity samples than on high proximity samples.
Probabilistic Knowledge Distillation of Face Ensembles
Mean ensemble (i. e. averaging predictions from multiple models) is a commonly-used technique in machine learning that improves the performance of each individual model.
Probabilistic Contrastive Loss for Self-Supervised Learning
This paper proposes a probabilistic contrastive loss function for self-supervised learning.
Spherical Confidence Learning for Face Recognition
Probabilistic Face Embeddings (PFE) is the first attempt to address this dilemma.
Hypersphere Face Uncertainty Learning
To address these issues, in this paper, we propose a novel framework for face uncertainty learning in hyperspherical space.
