77,400 research outputs found
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering
Deep metric learning has been widely applied in many computer vision tasks,
and recently, it is more attractive in \emph{zero-shot image retrieval and
clustering}(ZSRC) where a good embedding is requested such that the unseen
classes can be distinguished well. Most existing works deem this 'good'
embedding just to be the discriminative one and thus race to devise powerful
metric objectives or hard-sample mining strategies for leaning discriminative
embedding. However, in this paper, we first emphasize that the generalization
ability is a core ingredient of this 'good' embedding as well and largely
affects the metric performance in zero-shot settings as a matter of fact. Then,
we propose the Energy Confused Adversarial Metric Learning(ECAML) framework to
explicitly optimize a robust metric. It is mainly achieved by introducing an
interesting Energy Confusion regularization term, which daringly breaks away
from the traditional metric learning idea of discriminative objective devising,
and seeks to 'confuse' the learned model so as to encourage its generalization
ability by reducing overfitting on the seen classes. We train this confusion
term together with the conventional metric objective in an adversarial manner.
Although it seems weird to 'confuse' the network, we show that our ECAML indeed
serves as an efficient regularization technique for metric learning and is
applicable to various conventional metric methods. This paper empirically and
experimentally demonstrates the importance of learning embedding with good
generalization, achieving state-of-the-art performances on the popular CUB,
CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks.
\textcolor[rgb]{1, 0, 0}{Code available at http://www.bhchen.cn/}.Comment: AAAI 2019, Spotligh
On Nash Dynamics of Matching Market Equilibria
In this paper, we study the Nash dynamics of strategic interplays of n buyers
in a matching market setup by a seller, the market maker. Taking the standard
market equilibrium approach, upon receiving submitted bid vectors from the
buyers, the market maker will decide on a price vector to clear the market in
such a way that each buyer is allocated an item for which he desires the most
(a.k.a., a market equilibrium solution). While such equilibrium outcomes are
not unique, the market maker chooses one (maxeq) that optimizes its own
objective --- revenue maximization. The buyers in turn change bids to their
best interests in order to obtain higher utilities in the next round's market
equilibrium solution.
This is an (n+1)-person game where buyers place strategic bids to gain the
most from the market maker's equilibrium mechanism. The incentives of buyers in
deciding their bids and the market maker's choice of using the maxeq mechanism
create a wave of Nash dynamics involved in the market. We characterize Nash
equilibria in the dynamics in terms of the relationship between maxeq and mineq
(i.e., minimum revenue equilibrium), and develop convergence results for Nash
dynamics from the maxeq policy to a mineq solution, resulting an outcome
equivalent to the truthful VCG mechanism.
Our results imply revenue equivalence between maxeq and mineq, and address
the question that why short-term revenue maximization is a poor long run
strategy, in a deterministic and dynamic setting
Efficient high-capacity quantum secret sharing with two-photon entanglement
An efficient high-capacity quantum secret sharing scheme is proposed
following some ideas in quantum dense coding with two-photon entanglement. The
message sender, Alice prepares and measures the two-photon entangled states,
and the two agents, Bob and Charlie code their information on their photons
with four local unitary operations, which makes this scheme more convenient for
the agents than others. This scheme has a high intrinsic efficiency for qubits
and a high capacity.Comment: 5 pages, no figures. A inappreciable error is correcte
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