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Found 7 papers, 0 papers with code
Learning Embeddings for Product Visual Search with Triplet Loss and Online Sampling
Our approach significantly outperforms the state-of-the-art on the DeepFashion dataset.
VISER: Visual Self-Regularization
In this work, we propose the use of large set of unlabeled images as a source of regularization data for learning robust visual representation.
The New Modality: Emoji Challenges in Prediction, Anticipation, and Retrieval
Over the past decade, emoji have emerged as a new and widespread form of digital communication, spanning diverse social networks and spoken languages.
Tag Prediction at Flickr: a View from the Darkroom
Our results demonstrate that, for real-world datasets, training exclusively with this noisy data yields performance on par with the standard paradigm of first pre-training on clean data and then fine-tuning.
Group Sparse Coding with a Laplacian Scale Mixture Prior
We show that, due to the conjugacy of the Gamma prior, it is possible to derive efficient inference procedures for both the coefficients and the scale parameter.
An Homotopy Algorithm for the Lasso with Online Observations
It has been shown that the problem of $\ell_1$-penalized least-square regression commonly referred to as the Lasso or Basis Pursuit DeNoising leads to solutions that are sparse and therefore achieves model selection.
Learning Horizontal Connections in a Sparse Coding Model of Natural Images
It has been shown that adapting a dictionary of basis functions to the statistics of natural images so as to maximize sparsity in the coefficients results in a set of dictionary elements whose spatial properties resemble those of V1 (primary visual cortex) receptive fields.
