Improving Nonlinear Projection Heads using Pretrained Autoencoder Embeddings

Andreas Schliebitz, Heiko Tapken, Martin Atzmueller

Keywords: Nonlinear Projection Heads, Multilayer Perceptrons, Autoencoder Embeddings, SimCLR Framework, Contrastive Learning, Representation Learning

TL;DR: The paper improves SimCLR's projection head by incorporating pretrained autoencoder embeddings, architectural adjustments, and alternative activations, leading to up to 2.9% higher classification accuracy and a reduction in projection dimensionality.

Abstract: This empirical study aims at improving the effectiveness of the standard 2-layer MLP projection head $g(\cdot)$ featured in the SimCLR framework through the use of pretrained autoencoder embeddings. Given a contrastive learning task with a largely unlabeled image classification dataset, we first train a shallow autoencoder architecture and extract its compressed representations contained in the encoder's embedding layer. After freezing the weights within this pretrained layer, we use it as a drop-in replacement for the input layer of SimCLR's default projector. Additionally, we also apply further architectural changes to the projector by decreasing its width and changing its activation function. The different projection heads are then used to contrastively train and evaluate a feature extractor $f(\cdot)$ following the SimCLR protocol, while also examining the performance impact of $Z$-score normalized datasets. Our experiments indicate that using a pretrained autoencoder embedding in the projector can not only increase classification accuracy by up to 2.9% or 1.7% on average but can also significantly decrease the dimensionality of the projection space. Our results also suggest, that using the sigmoid and $\tanh$ activation functions within the projector can outperform ReLU in terms of peak and average classification accuracy. When applying our presented projectors, then not applying $Z$-score normalization to datasets often increases peak performance. In contrast, the default projection head can benefit more from normalization. All experiments involving our pretrained projectors are conducted with frozen embeddings, since our test results indicate an advantage compared to using their non-frozen counterparts.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 9579