6 research outputs found
Classification of rare traffic signs
Π ΡΠ°Π±ΠΎΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΡΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΡ
ΠΌΠ°Π»ΠΎ ΠΈΠ»ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌ Π½Π΅Ρ Π² ΠΎΠ±ΡΡΠ°ΡΡΠ΅ΠΉ Π²ΡΠ±ΠΎΡΠΊΠ΅, Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ Π·Π°Π΄Π°ΡΠΈ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ΅Π΄ΠΊΠΈΡ
Π΄ΠΎΡΠΎΠΆΠ½ΡΡ
Π·Π½Π°ΠΊΠΎΠ². Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠ΅ ΡΠ΅ΡΠΈ, ΠΎΠ±ΡΡΠ΅Π½Π½ΡΠ΅ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΡΠ°Π²Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΠΏΠΎΡΠ΅ΡΡ ΠΈ Π΅Ρ ΠΌΠΎΠ΄ΠΈΡΠΈΠΊΠ°ΡΠΈΡΠΌΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΡΠΈΠ½ΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²ΡΠ±ΠΎΡΠΎΠΊ Π΄Π»Ρ Π·Π°Π΄Π°Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ Π±Π°Π·ΠΎΠ²ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΈΠ½Π΄Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠ»Π°ΡΡΠΎΠ² ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ². ΠΡΠΎΠ²ΠΎΠ΄ΠΈΡΡΡ ΡΡΠ°Π²Π½Π΅Π½ΠΈΠ΅ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠ², ΠΎΠ±ΡΡΠ΅Π½Π½ΡΡ
ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ ΡΡΡΡ
Π²ΠΈΠ΄ΠΎΠ² ΡΠΈΠ½ΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π²ΡΠ±ΠΎΡΠΎΠΊ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΡ
ΡΠΌΠ΅ΡΠ΅ΠΉ Ρ ΡΠ΅Π°Π»ΡΠ½ΡΠΌΠΈ Π΄Π°Π½Π½ΡΠΌΠΈ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ΅Π΄ΠΊΠΈΡ
Π΄ΠΎΡΠΎΠΆΠ½ΡΡ
Π·Π½Π°ΠΊΠΎΠ², ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΠΈΠΉ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅Π²ΠΎΠΉ Π΄ΠΈΡΠΊΡΠΈΠΌΠΈΠ½Π°ΡΠΎΡ ΡΠ΅Π΄ΠΊΠΈΡ
ΠΈ ΡΠ°ΡΡΡΡ
Π·Π½Π°ΠΊΠΎΠ². ΠΡΠΎΠ²Π΅Π΄ΡΠ½Π½Π°Ρ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° ΠΏΠΎΠΊΠ°Π·Π°Π»Π°, ΡΡΠΎ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°ΡΡ ΡΠ΅Π΄ΠΊΠΈΠ΅ Π΄ΠΎΡΠΎΠΆΠ½ΡΠ΅ Π·Π½Π°ΠΊΠΈ Π±Π΅Π· ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΏΠΎΡΠ΅ΡΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π½Π° ΡΠ°ΡΡΡΡ
Π·Π½Π°ΠΊΠ°Ρ
.Π Π°Π±ΠΎΡΠ° Π.Π. Π‘Π°Π½ΠΆΠ°ΡΠΎΠ²Π° ΠΏΠΎ ΡΠΎΡΠΎΡΠ΅Π°Π»ΠΈΡΡΠΈΡΠ½ΠΎΠΌΡ ΡΠΈΠ½ΡΠ΅Π·Ρ Π΄ΠΎΡΠΎΠΆΠ½ΡΡ
Π·Π½Π°ΠΊΠΎΠ² Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° ΠΏΡΠΈ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ Π³ΡΠ°Π½ΡΠ° Π Π€Π€Π 18-31-20032 ΠΌΠΎΠ»_Π°_Π²Π΅Π΄ Β«Π€ΠΈΠ·ΠΈΡΠ΅ΡΠΊΠΈ-ΠΊΠΎΡΡΠ΅ΠΊΡΠ½ΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΡΠ²Π΅ΡΠ΅Π½ΠΈΡ ΠΈ ΡΠΈΠ½ΡΠ΅Π· ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π½Π° ΠΌΠ°ΡΡΠΈΠ²Π½ΠΎ-ΠΏΠ°ΡΠ°Π»Π»Π΅Π»ΡΠ½ΡΡ
Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
Π² ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡΡ
ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ°Β», ΡΠ°Π±ΠΎΡΠ° Π.Π. Π€Π°ΠΈΠ·ΠΎΠ²Π°, Π.Π. Π¨Π°Ρ
ΡΡΠΎ ΠΈ Π.Π‘. ΠΠΎΠ½ΡΡΠΈΠ½Π° ΠΏΠΎ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ Π΄ΠΎΡΠΎΠΆΠ½ΡΡ
Π·Π½Π°ΠΊΠΎΠ² Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅ΠΉ ΠΈ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ΅Π΄ΠΊΠΈΡ
Π·Π½Π°ΠΊΠΎΠ² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠ°Π½Π° Π³ΡΠ°Π½ΡΠΎΠΌ Π ΠΠ€ 17-71-20072 Β«ΠΠ΅ΠΉΡΠΎΠ±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π² Π·Π°Π΄Π°ΡΠ°Ρ
ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, ΠΌΠ°ΡΡΡΠ°Π±ΠΈΡΡΠ΅ΠΌΠΎΠΉ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·ΡΠ΅Π½ΠΈΡΒ»
The Catalog Problem:Deep Learning Methods for Transforming Sets into Sequences of Clusters
The titular Catalog Problem refers to predicting a varying number of ordered clusters from sets of any cardinality. This task arises in many diverse areas, ranging from medical triage, through multi-channel signal analysis for petroleum exploration to product catalog structure prediction. This thesis focuses on the latter, which exemplifies a number of challenges inherent to ordered clustering. These include learning variable cluster constraints, exhibiting relational reasoning and managing combinatorial complexity. All of which present unique challenges for neural networks, combining elements of set representation, neural clustering and permutation learning.In order to approach the Catalog Problem, a curated dataset of over ten thousand real-world product catalogs consisting of more than one million product offers is provided. Additionally, a library for generating simpler, synthetic catalog structures is presented. These and other datasets form the foundation of the included work, allowing for a quantitative comparison of the proposed methodsβ ability to address the underlying challenge. In particular, synthetic datasets enable the assessment of the modelsβ capacity to learn higher order compositional and structural rules.Two novel neural methods are proposed to tackle the Catalog Problem, a set encoding module designed to enhance the networkβs ability to condition the prediction on the entirety of the input set, and a larger architecture for inferring an input- dependent number of diverse, ordered partitional clusters with an added cardinality prediction module. Both result in an improved performance on the presented datasets, with the latter being the only neural method fulfilling all requirements inherent to addressing the Catalog Problem