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Found 9 papers, 2 papers with code
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer
Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images.
Random Field Augmentations for Self-Supervised Representation Learning
In this work, we propose a new family of local transformations based on Gaussian random fields to generate image augmentations for self-supervised representation learning.
Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout
To mitigate this issue and facilitate training of large models on edge devices, we introduce a simple yet effective strategy, Federated Layer-wise Learning, to simultaneously reduce per-client memory, computation, and communication costs.
Federated Variational Inference: Towards Improved Personalization and Generalization
We then approximate this process using Variational Inference to train our model efficiently.
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets
In this work, we focus on federated training of dual encoding models on decentralized data composed of many small, non-IID (independent and identically distributed) client datasets.
Camera View Adjustment Prediction for Improving Image Composition
To address this issue, we propose a deep learning-based approach that provides suggestions to the photographer on how to adjust the camera view before capturing.
Contrastive Learning for Label Efficient Semantic Segmentation
While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled training data, their performance drops significantly as the amount of labeled data decreases.
Links: A High-Dimensional Online Clustering Method
We present a novel algorithm, called Links, designed to perform online clustering on unit vectors in a high-dimensional Euclidean space.
Speaker Diarization with LSTM
For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications.
Ranked #2 on
Speaker Diarization
on CALLHOME-109
