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Enriching Word Vectors with Subword Information
TLDR
A new approach based on the skipgram model, where each word is represented as a bag of character n-grams, with words being represented as the sum of these representations, which achieves state-of-the-art performance on word similarity and analogy tasks.
Bag of Tricks for Efficient Text Classification
TLDR
A simple and efficient baseline for text classification is explored that shows that the fast text classifier fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation.
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
TLDR
This paper proposes an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons, and uses a swapped prediction mechanism where it predicts the cluster assignment of a view from the representation of another view.
Deep Clustering for Unsupervised Learning of Visual Features
TLDR
This work presents DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features and outperforms the current state of the art by a significant margin on all the standard benchmarks.
Learning Word Vectors for 157 Languages
TLDR
This paper describes how high quality word representations for 157 languages were trained on the free online encyclopedia Wikipedia and data from the common crawl project, and introduces three new word analogy datasets to evaluate these word vectors.
Emerging Properties in Self-Supervised Vision Transformers
TLDR
This paper questions if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets) and implements DINO, a form of self-distillation with no labels, which implements the synergy between DINO and ViTs.
Discriminative clustering for image co-segmentation
TLDR
This paper combines existing tools for bottom-up image segmentation such as normalized cuts, with kernel methods commonly used in object recognition, used within a discriminative clustering framework to obtain a combinatorial optimization problem which is relaxed to a continuous convex optimization problem that can be solved efficiently for up to dozens of images.
Unsupervised Joint Object Discovery and Segmentation in Internet Images
TLDR
This work proposes to use dense correspondences between images to capture the sparsity and visual variability of the common object over the entire database, which enables us to ignore noise objects that may be salient within their own images but do not commonly occur in others.
FastText.zip: Compressing text classification models
TLDR
This work proposes a method built upon product quantization to store the word embeddings, which produces a text classifier, derived from the fastText approach, which at test time requires only a fraction of the memory compared to the original one, without noticeably sacrificing the quality in terms of classification accuracy.
Advances in Pre-Training Distributed Word Representations
TLDR
This paper shows how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together to outperform the current state of the art by a large margin on a number of tasks.
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