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Tianhong Li
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Contrastive learning is one of the fastest growing research areas in machine learning due to its ability to learn useful representations without labeled data. However, contrastive learning is susceptible to shortcuts - i.e., it may learn shortcut features irrelevant to the task of interest, and discard relevant information. Past work has addressed this limitation via handcrafted data augmentations that eliminate the shortcut. But, manually crafted augmentations do not work across all datasets and tasks. Further, data augmentations fail in addressing shortcuts in multi-attribute classification when one attribute acts as a shortcut around other attributes. In this paper, we analyze the objective function of contrastive learning and formally prove that it is vulnerable to shortcuts. We then present reconstructive contrastive learning (RCL), a framework for learning unsupervised representations that are robust to shortcuts. The key idea is to force the learned representation to reconstruct the input, which naturally counters potential shortcuts. Extensive experiments verify that RCL is highly robust to shortcuts and outperforms state-of-the-art contrastive learning methods on a variety of datasets and tasks.
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Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models. Modern batch contrastive approaches subsume or significantly outperform traditional contrastive losses such as triplet, max-margin and the N-pairs loss. In this work, we extend the self-supervised batch contrastive approach to the fully-supervised setting, allowing us to effectively leverage label information. Clusters of points belonging to the same class are pulled together in embedding space, while simultaneously pushing apart clusters of samples from different classes. We analyze two possib
With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multiple modalities and characterized with multiple labels, thus exhibiting the co-existence of multiple types of heterogeneity. Although state-of-the-art techniques are good at modeling the complex heterogeneity with sufficient label information, such label information can be quite expensive to obtain in real applications, leading to sub-optimal performance using these techniques. Inspired by the capability of contrastive learning to utilize rich unlabeled data for improving performance, in this paper, we propose a unified heterogeneous learning framework, which combines both weighted unsupervised contrastive loss and weighted supervised contrastive loss to model multiple types of heterogeneity. We also provide theoretical analyses showing that the proposed weighted supervised contrastive loss is the lower bound of the mutual information of two samples from the same class and the weighted unsupervised contrastive loss is the lower bound of the mutual information between the hidden representation of two views of the same sample. Experimental results on real-world data sets demonstrate the effectiveness and the efficiency of the proposed method modeling multiple types of heterogeneity.
Contrastive learning (CL) is effective in learning data representations without label supervision, where the encoder needs to contrast each positive sample over multiple negative samples via a one-vs-many softmax cross-entropy loss. However, conventional CL is sensitive to how many negative samples are included and how they are selected. Proposed in this paper is a doubly CL strategy that contrasts positive samples and negative ones within themselves separately. We realize this strategy with contrastive attraction and contrastive repulsion (CACR) makes the query not only exert a greater force to attract more distant positive samples but also do so to repel closer negative samples. Theoretical analysis reveals the connection between CACR and CL from the perspectives of both positive attraction and negative repulsion and shows the benefits in both efficiency and robustness brought by separately contrasting within the sampled positive and negative pairs. Extensive large-scale experiments on standard vision tasks show that CACR not only consistently outperforms existing CL methods on benchmark datasets in representation learning, but also provides interpretable contrastive weights, demonstrating the efficacy of the proposed doubly contrastive strategy.
Deep neural nets typically perform end-to-end backpropagation to learn the weights, a procedure that creates synchronization constraints in the weight update step across layers and is not biologically plausible. Recent advances in unsupervised contrastive representation learning point to the question of whether a learning algorithm can also be made local, that is, the updates of lower layers do not directly depend on the computation of upper layers. While Greedy InfoMax separately learns each block with a local objective, we found that it consistently hurts readout accuracy in state-of-the-art unsupervised contrastive learning algorithms, possibly due to the greedy objective as well as gradient isolation. In this work, we discover that by overlapping local blocks stacking on top of each other, we effectively increase the decoder depth and allow upper blocks to implicitly send feedbacks to lower blocks. This simple design closes the performance gap between local learning and end-to-end contrastive learning algorithms for the first time. Aside from standard ImageNet experiments, we also show results on complex downstream tasks such as object detection and instance segmentation directly using readout features.
Recent breakthroughs in self-supervised learning show that such algorithms learn visual representations that can be transferred better to unseen tasks than joint-training methods relying on task-specific supervision. In this paper, we found that the similar holds in the continual learning con-text: contrastively learned representations are more robust against the catastrophic forgetting than jointly trained representations. Based on this novel observation, we propose a rehearsal-based continual learning algorithm that focuses on continually learning and maintaining transferable representations. More specifically, the proposed scheme (1) learns representations using the contrastive learning objective, and (2) preserves learned representations using a self-supervised distillation step. We conduct extensive experimental validations under popular benchmark image classification datasets, where our method sets the new state-of-the-art performance.
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