Search Results for author: Joao F. Henriques

Found 7 papers, 2 papers with code

Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes

no code implementations5 May 2021 Dan Xu, Andrea Vedaldi, Joao F. Henriques

We build on the idea of view synthesis, which uses classical camera geometry to re-render a source image from a different point-of-view, specified by a predicted relative pose and depth map.

Depth Estimation Semantic Segmentation

On the Origin of Species of Self-Supervised Learning

no code implementations31 Mar 2021 Samuel Albanie, Erika Lu, Joao F. Henriques

In the quiet backwaters of cs. CV, cs. LG and stat. ML, a cornucopia of new learning systems is emerging from a primordial soup of mathematics-learning systems with no need for external supervision.

Self-Supervised Learning

Small Steps and Giant Leaps: Minimal Newton Solvers for Deep Learning

1 code implementation ICCV 2019 Joao F. Henriques, Sebastien Ehrhardt, Samuel Albanie, Andrea Vedaldi

We first validate our method, called CurveBall, on small problems with known solutions (noisy Rosenbrock function and degenerate 2-layer linear networks), where current deep learning solvers struggle.

Substitute Teacher Networks: Learning with Almost No Supervision

1 code implementation1 Apr 2018 Samuel Albanie, James Thewlis, Joao F. Henriques

Learning through experience is time-consuming, inefficient and often bad for your cortisol levels.

Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem

no code implementations CVPR 2013 Rui Caseiro, Pedro Martins, Joao F. Henriques, Fatima Silva Leite, Jorge Batista

In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds [31, 27].

Classification General Classification +1

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