Bottleneck-based Encoder-decoder ARchitecture (BEAR) for Learning Unbiased Consumer-to-Consumer Image Representations
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913495479484416 |
|---|---|
| author | Rivas, Pablo Bichler, Gisela Cerny, Tomas Giddens, Laurie Petter, Stacie |
| author_facet | Rivas, Pablo Bichler, Gisela Cerny, Tomas Giddens, Laurie Petter, Stacie |
| contents | Unbiased representation learning is still an object of study under specific applications and contexts. Novel architectures are usually crafted to resolve particular problems using mixtures of fundamental pieces. This paper presents different image feature extraction mechanisms that work together with residual connections to encode perceptual image information in an autoencoder configuration. We use image data that aims to support a larger research agenda dealing with issues regarding criminal activity in consumer-to-consumer online platforms. Preliminary results suggest that the proposed architecture can learn rich spaces using ours and other image datasets resolving important challenges that are identified. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06187 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bottleneck-based Encoder-decoder ARchitecture (BEAR) for Learning Unbiased Consumer-to-Consumer Image Representations Rivas, Pablo Bichler, Gisela Cerny, Tomas Giddens, Laurie Petter, Stacie Computer Vision and Pattern Recognition Machine Learning I.2.10; I.5.1; K.4.1; H.3.3; I.2.6 Unbiased representation learning is still an object of study under specific applications and contexts. Novel architectures are usually crafted to resolve particular problems using mixtures of fundamental pieces. This paper presents different image feature extraction mechanisms that work together with residual connections to encode perceptual image information in an autoencoder configuration. We use image data that aims to support a larger research agenda dealing with issues regarding criminal activity in consumer-to-consumer online platforms. Preliminary results suggest that the proposed architecture can learn rich spaces using ours and other image datasets resolving important challenges that are identified. |
| title | Bottleneck-based Encoder-decoder ARchitecture (BEAR) for Learning Unbiased Consumer-to-Consumer Image Representations |
| topic | Computer Vision and Pattern Recognition Machine Learning I.2.10; I.5.1; K.4.1; H.3.3; I.2.6 |
| url | https://arxiv.org/abs/2409.06187 |