29 research outputs found
Bounded-Distortion Metric Learning
Metric learning aims to embed one metric space into another to benefit tasks
like classification and clustering. Although a greatly distorted metric space
has a high degree of freedom to fit training data, it is prone to overfitting
and numerical inaccuracy. This paper presents {\it bounded-distortion metric
learning} (BDML), a new metric learning framework which amounts to finding an
optimal Mahalanobis metric space with a bounded-distortion constraint. An
efficient solver based on the multiplicative weights update method is proposed.
Moreover, we generalize BDML to pseudo-metric learning and devise the
semidefinite relaxation and a randomized algorithm to approximately solve it.
We further provide theoretical analysis to show that distortion is a key
ingredient for stability and generalization ability of our BDML algorithm.
Extensive experiments on several benchmark datasets yield promising results
A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases
Deep language models learning a hierarchical representation proved to be a
powerful tool for natural language processing, text mining and information
retrieval. However, representations that perform well for retrieval must
capture semantic meaning at different levels of abstraction or context-scopes.
In this paper, we propose a new method to generate multi-resolution word
embeddings that represent documents at multiple resolutions in terms of
context-scopes. In order to investigate its performance,we use the Stanford
Question Answering Dataset (SQuAD) and the Question Answering by Search And
Reading (QUASAR) in an open-domain question-answering setting, where the first
task is to find documents useful for answering a given question. To this end,
we first compare the quality of various text-embedding methods for retrieval
performance and give an extensive empirical comparison with the performance of
various non-augmented base embeddings with and without multi-resolution
representation. We argue that multi-resolution word embeddings are consistently
superior to the original counterparts and deep residual neural models
specifically trained for retrieval purposes can yield further significant gains
when they are used for augmenting those embeddings
Effective Face Frontalization in Unconstrained Images
"Frontalization" is the process of synthesizing frontal facing views of faces
appearing in single unconstrained photos. Recent reports have suggested that
this process may substantially boost the performance of face recognition
systems. This, by transforming the challenging problem of recognizing faces
viewed from unconstrained viewpoints to the easier problem of recognizing faces
in constrained, forward facing poses. Previous frontalization methods did this
by attempting to approximate 3D facial shapes for each query image. We observe
that 3D face shape estimation from unconstrained photos may be a harder problem
than frontalization and can potentially introduce facial misalignments.
Instead, we explore the simpler approach of using a single, unmodified, 3D
surface as an approximation to the shape of all input faces. We show that this
leads to a straightforward, efficient and easy to implement method for
frontalization. More importantly, it produces aesthetic new frontal views and
is surprisingly effective when used for face recognition and gender estimation