15,947 research outputs found
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
Ranking-based Deep Cross-modal Hashing
Cross-modal hashing has been receiving increasing interests for its low
storage cost and fast query speed in multi-modal data retrievals. However, most
existing hashing methods are based on hand-crafted or raw level features of
objects, which may not be optimally compatible with the coding process.
Besides, these hashing methods are mainly designed to handle simple pairwise
similarity. The complex multilevel ranking semantic structure of instances
associated with multiple labels has not been well explored yet. In this paper,
we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH
firstly uses the feature and label information of data to derive a
semi-supervised semantic ranking list. Next, to expand the semantic
representation power of hand-crafted features, RDCMH integrates the semantic
ranking information into deep cross-modal hashing and jointly optimizes the
compatible parameters of deep feature representations and of hashing functions.
Experiments on real multi-modal datasets show that RDCMH outperforms other
competitive baselines and achieves the state-of-the-art performance in
cross-modal retrieval applications
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
Hash codes are efficient data representations for coping with the ever
growing amounts of data. In this paper, we introduce a random forest semantic
hashing scheme that embeds tiny convolutional neural networks (CNN) into
shallow random forests, with near-optimal information-theoretic code
aggregation among trees. We start with a simple hashing scheme, where random
trees in a forest act as hashing functions by setting `1' for the visited tree
leaf, and `0' for the rest. We show that traditional random forests fail to
generate hashes that preserve the underlying similarity between the trees,
rendering the random forests approach to hashing challenging. To address this,
we propose to first randomly group arriving classes at each tree split node
into two groups, obtaining a significantly simplified two-class classification
problem, which can be handled using a light-weight CNN weak learner. Such
random class grouping scheme enables code uniqueness by enforcing each class to
share its code with different classes in different trees. A non-conventional
low-rank loss is further adopted for the CNN weak learners to encourage code
consistency by minimizing intra-class variations and maximizing inter-class
distance for the two random class groups. Finally, we introduce an
information-theoretic approach for aggregating codes of individual trees into a
single hash code, producing a near-optimal unique hash for each class. The
proposed approach significantly outperforms state-of-the-art hashing methods
for image retrieval tasks on large-scale public datasets, while performing at
the level of other state-of-the-art image classification techniques while
utilizing a more compact and efficient scalable representation. This work
proposes a principled and robust procedure to train and deploy in parallel an
ensemble of light-weight CNNs, instead of simply going deeper.Comment: Accepted to ECCV 201
Unsupervised Learning of Semantic Audio Representations
Even in the absence of any explicit semantic annotation, vast collections of
audio recordings provide valuable information for learning the categorical
structure of sounds. We consider several class-agnostic semantic constraints
that apply to unlabeled nonspeech audio: (i) noise and translations in time do
not change the underlying sound category, (ii) a mixture of two sound events
inherits the categories of the constituents, and (iii) the categories of events
in close temporal proximity are likely to be the same or related. Without
labels to ground them, these constraints are incompatible with classification
loss functions. However, they may still be leveraged to identify geometric
inequalities needed for triplet loss-based training of convolutional neural
networks. The result is low-dimensional embeddings of the input spectrograms
that recover 41% and 84% of the performance of their fully-supervised
counterparts when applied to downstream query-by-example sound retrieval and
sound event classification tasks, respectively. Moreover, in
limited-supervision settings, our unsupervised embeddings double the
state-of-the-art classification performance.Comment: Submitted to ICASSP 201
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
This paper addresses the task of zero-shot image classification. The key
contribution of the proposed approach is to control the semantic embedding of
images -- one of the main ingredients of zero-shot learning -- by formulating
it as a metric learning problem. The optimized empirical criterion associates
two types of sub-task constraints: metric discriminating capacity and accurate
attribute prediction. This results in a novel expression of zero-shot learning
not requiring the notion of class in the training phase: only pairs of
image/attributes, augmented with a consistency indicator, are given as ground
truth. At test time, the learned model can predict the consistency of a test
image with a given set of attributes , allowing flexible ways to produce
recognition inferences. Despite its simplicity, the proposed approach gives
state-of-the-art results on four challenging datasets used for zero-shot
recognition evaluation.Comment: in ECCV 2016, Oct 2016, amsterdam, Netherlands. 201
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