113 research outputs found
CIFAR-10: KNN-based Ensemble of Classifiers
In this paper, we study the performance of different classifiers on the
CIFAR-10 dataset, and build an ensemble of classifiers to reach a better
performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and
Convolutional Neural Network (CNN), on some classes, are mutually exclusive,
thus yield in higher accuracy when combined. We reduce KNN overfitting using
Principal Component Analysis (PCA), and ensemble it with a CNN to increase its
accuracy. Our approach improves our best CNN model from 93.33% to 94.03%
Prototypical Contrastive Learning of Unsupervised Representations
This paper presents Prototypical Contrastive Learning (PCL), an unsupervised
representation learning method that addresses the fundamental limitations of
instance-wise contrastive learning. PCL not only learns low-level features for
the task of instance discrimination, but more importantly, it implicitly
encodes semantic structures of the data into the learned embedding space.
Specifically, we introduce prototypes as latent variables to help find the
maximum-likelihood estimation of the network parameters in an
Expectation-Maximization framework. We iteratively perform E-step as finding
the distribution of prototypes via clustering and M-step as optimizing the
network via contrastive learning. We propose ProtoNCE loss, a generalized
version of the InfoNCE loss for contrastive learning, which encourages
representations to be closer to their assigned prototypes. PCL outperforms
state-of-the-art instance-wise contrastive learning methods on multiple
benchmarks with substantial improvement in low-resource transfer learning. Code
and pretrained models are available at https://github.com/salesforce/PCL
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