563 research outputs found
Collaborative Feature Learning from Social Media
Image feature representation plays an essential role in image recognition and
related tasks. The current state-of-the-art feature learning paradigm is
supervised learning from labeled data. However, this paradigm requires
large-scale category labels, which limits its applicability to domains where
labels are hard to obtain. In this paper, we propose a new data-driven feature
learning paradigm which does not rely on category labels. Instead, we learn
from user behavior data collected on social media. Concretely, we use the image
relationship discovered in the latent space from the user behavior data to
guide the image feature learning. We collect a large-scale image and user
behavior dataset from Behance.net. The dataset consists of 1.9 million images
and over 300 million view records from 1.9 million users. We validate our
feature learning paradigm on this dataset and find that the learned feature
significantly outperforms the state-of-the-art image features in learning
better image similarities. We also show that the learned feature performs
competitively on various recognition benchmarks
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
Context plays an important role in human language understanding, thus it may
also be useful for machines learning vector representations of language. In
this paper, we explore an asymmetric encoder-decoder structure for unsupervised
context-based sentence representation learning. We carefully designed
experiments to show that neither an autoregressive decoder nor an RNN decoder
is required. After that, we designed a model which still keeps an RNN as the
encoder, while using a non-autoregressive convolutional decoder. We further
combine a suite of effective designs to significantly improve model efficiency
while also achieving better performance. Our model is trained on two different
large unlabelled corpora, and in both cases the transferability is evaluated on
a set of downstream NLP tasks. We empirically show that our model is simple and
fast while producing rich sentence representations that excel in downstream
tasks
Rethinking Skip-thought: A Neighborhood based Approach
We study the skip-thought model with neighborhood information as weak
supervision. More specifically, we propose a skip-thought neighbor model to
consider the adjacent sentences as a neighborhood. We train our skip-thought
neighbor model on a large corpus with continuous sentences, and then evaluate
the trained model on 7 tasks, which include semantic relatedness, paraphrase
detection, and classification benchmarks. Both quantitative comparison and
qualitative investigation are conducted. We empirically show that, our
skip-thought neighbor model performs as well as the skip-thought model on
evaluation tasks. In addition, we found that, incorporating an autoencoder path
in our model didn't aid our model to perform better, while it hurts the
performance of the skip-thought model
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