23 citations · 23 across the 2 of their papers we have counts for
6 papers
Uncertainty Surrogates for Deep Learning
Radhakrishna Achanta, Natasa Tagasovska
In this paper we introduce a novel way of estimating prediction uncertainty in deep networks through the use of uncertainty surrogates. These surrogates are features of the penulti…
Image Obfuscation for Privacy-Preserving Machine Learning
Mathilde Raynal, Radhakrishna Achanta, Mathias Humbert
Privacy becomes a crucial issue when outsourcing the training of machine learning (ML) models to cloud-based platforms offering machine-learning services. While solutions based on…
Self-Binarizing Networks
Fayez Lahoud, Radhakrishna Achanta, Pablo Márquez-Neila +1
We present a method to train self-binarizing neural networks, that is, networks that evolve their weights and activations during training to become binary. To obtain similar binary…
Fourier-Domain Optimization for Image Processing
Majed El Helou, Frederike Dümbgen, Radhakrishna Achanta +1
Image optimization problems encompass many applications such as spectral fusion, deblurring, deconvolution, dehazing, matting, reflection removal and image interpolation, among oth…
Deep Feature Factorization For Concept Discovery
Edo Collins, Radhakrishna Achanta, Sabine Süsstrunk
We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep con…
Deep Residual Network for Joint Demosaicing and Super-Resolution
Ruofan Zhou, Radhakrishna Achanta, Sabine Süsstrunk
In digital photography, two image restoration tasks have been studied extensively and resolved independently: demosaicing and super-resolution. Both these tasks are related to reso…