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1. X -Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs

   Sobal, Vlad; Ibrahim, Mark; Balestriero, Randall; Cabannes, Vivien; Bouchacourt, Diane; Astolfi, Pietro; Cho, Kyunghyun; LeCun, Yann (April 2025, The International Conference on Learning Representations (ICLR 2025))

   Learning good representations involves capturing the diverse ways in which data samples relate. Contrastive loss - an objective matching related samples - underlies methods from self-supervised to multimodal learning. Contrastive losses, however, can be viewed more broadly as modifying a similarity graph to indicate how samples should relate in the embedding space. This view reveals a shortcoming in contrastive learning: the similarity graph is binary, as only one sample is the related positive sample. Crucially, similarities \textit{across} samples are ignored. Based on this observation, we revise the standard contrastive loss to explicitly encode how a sample relates to others. We experiment with this new objective, called X -Sample Contrastive, to train vision models based on similarities in class or text caption descriptions. Our study spans three scales: ImageNet-1k with 1 million, CC3M with 3 million, and CC12M with 12 million samples. The representations learned via our objective outperform both contrastive self-supervised and vision-language models trained on the same data across a range of tasks. When training on CC12M, we outperform CLIP by on both ImageNet and ImageNet Real. Our objective appears to work particularly well in lower-data regimes, with gains over CLIP of on ImageNet and on ImageNet Real when training with CC3M. Finally, our objective seems to encourage the model to learn representations that separate objects from their attributes and backgrounds, with gains of - \% over CLIP on ImageNet9. We hope the proposed solution takes a small step towards developing richer learning objectives for understanding sample relations in foundation models. 

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2. Contrastive Unlearning: A Contrastive Approach to Machine Unlearning

   https://doi.org/10.24963/ijcai.2025/830  

   Lee, Hong kyu; Zhang, Qiuchen; Yang, Carl; Lou, Jian; Xiong, Li (September 2025, International Joint Conferences on Artificial Intelligence Organization)

   Machine unlearning aims to eliminate the influence of a subset of training samples (i.e., unlearning samples) from a trained model. Effectively and efficiently removing the unlearning samples without negatively impacting the overall model performance is challenging. Existing works mainly exploit input and output space and classification loss, which can result in ineffective unlearning or performance loss. In addition, they utilize unlearning or remaining samples ineffectively, sacrificing either unlearning efficacy or efficiency. Our main insight is that the direct optimization on the representation space utilizing both unlearning and remaining samples can effectively remove influence of unlearning samples while maintaining representations learned from remaining samples. We propose a contrastive unlearning framework, leveraging the concept of representation learning for more effective unlearning. It removes the influence of unlearning samples by contrasting their embeddings against the remaining samples' embeddings so that their embeddings are closer to the embeddings of unseen samples. Experiments on a variety of datasets and models on both class unlearning and sample unlearning showed that contrastive unlearning achieves the best unlearning effects and efficiency with the lowest performance loss compared with the state-of-the-art algorithms. In addition, it is generalizable to different contrastive frameworks and other models such as vision-language models. Our main code is available on github.com/Emory-AIMS/Contrastive-Unlearning 

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3. Enhancing Contrastive Representation Learning through Data

   Zhang, Honggen (May 2025, ProQuest Dissertation)

   Contrastive learning learns input representation by pushing similar data together and pulling dissimilar data away, along with data augmentation and pretext task construction. It enhances the large model learning due to its ability to use a large amount of unlabeled data. It has been suc- cessfully applied to large language models, pre-trained image models, and multimodal models. In addition, contrastive learning learns a representation from modeling the explainable structure of the latent space, which has a broad application in scientific discovery and interpretable Artificial Intelligence (AI). The primary focus of this thesis is to explore contrastive learning from a data construction perspective in real-world problems to fill the gap between the principle of contrastive learning and its application. The challenges, such as sampling bias and data quality, will largely affect the representations learned by contrastive learning. This thesis analyzes the data construction chanlledges and limitations in 1) the negative sampling of knowledge graph embedding (KGE), 2) high-quliaty preference data labeling of Large Language Models (LLMs) alignment, 3) data augmentation in Non-linear dynamic system modeling, and 4) data properties in functions of mesange RNA (mRNA) sequence. To solve the challenges 1), a hardness and structure-based objective function was proposed by considering sampling bias in hard negative sampling. For challenge 2), the similarity of response embedding is used to evaluate the quality of preference pairs to mitigate the labeling error of humans when they face an ambiguous response pair. Chal- lenge 3) is solved by systematically considering the physical system and contrastive learning. A data augmentation strategy by partitioning the full sequence is used for learning the transition matrix in the latent linear space. Challenge 4) is common to see in the biological domain due to the high cost of lab experiments. Pre-trained model will advantage the limited dataset su- pervised learning by learning general features from domain knowledge. A contrastive learning based teacher-student framework is proposed for mRNA sequence learning by contrasting the unmasked sequence and the hard-masked sequence. By providing careful data construction or data sampling, contrastive learning will be boosted to solve tasks in reality. For the KGE, the novel contrastive loss function learns the boundary between negative samples and positive samples to improve the link prediction task in the knowl- edge graph; For the LLM alignment, in the same labeling cost, the selected dissimilar responses will improve the vanilla direct preference optimization (DPO) alignment; The data augmentation with contrastive loss play crucial role to learn more accuracy dynamic system, which explained by the learned the continiues eigenfunction; By considering the tearch-student framework with hard-masked strategy, the pre-trained model achieve the state-of-the-art result by fine-tuning on limited downstrame task data. Overall, this thesis provides a broad data-driven contrastive learning methodology to enhance representation learning in different domains. The methodology consists of a imprived objective function in the face of data bias, a better data selection reducing labeling error, and proper data augmentation for a particular application domain. This methodology improve the learning result compare to traditional method. 

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4. Slimmed Asymmetrical Contrastive Learning and Cross Distillation for Lightweight Model Training

   Meng, Jian; Yang, Li; Lee, Kyungmin; Shin, Jinwoo; Fan, Deliang; Seo, Jae-sun (December 2023, Thirty-seventh Conference on Neural Information Processing Systems)

   Contrastive learning (CL) has been widely investigated with various learning mech- anisms and achieves strong capability in learning representations of data in a self-supervised manner using unlabeled data. A common fashion of contrastive learning on this line is employing large-sized encoders to achieve comparable performance as the supervised learning counterpart. Despite the success of the labelless training, current contrastive learning algorithms failed to achieve good performance with lightweight (compact) models, e.g., MobileNet, while the re- quirements of the heavy encoders impede the energy-efficient computation, espe- cially for resource-constrained AI applications. Motivated by this, we propose a new self-supervised CL scheme, named SACL-XD, consisting of two technical components, Slimmed Asymmetrical Contrastive Learning (SACL) and Cross- Distillation (XD), which collectively enable efficient CL with compact models. While relevant prior works employed a strong pre-trained model as the teacher of unsupervised knowledge distillation to a lightweight encoder, our proposed method trains CL models from scratch and outperforms them even without such an expensive requirement. Compared to the SoTA lightweight CL training (dis- tillation) algorithms, SACL-XD achieves 1.79% ImageNet-1K accuracy improve- ment on MobileNet-V3 with 64⇥ training FLOPs reduction. Code is available at https://github.com/mengjian0502/SACL-XD. 

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5. Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities

   Saporta, Adriel; Puli, Aahlad; Goldstein, Mark; Ranganath, Rajesh (December 2024, 38th Conference on Neural Information Processing Systems (NeurIPS 2024))

   Contrastive learning methods, such as CLIP, leverage naturally paired data-for example, images and their corresponding text captions-to learn general representations that transfer efficiently to downstream tasks. While such approaches are generally applied to two modalities, domains such as robotics, healthcare, and video need to support many types of data at once. We show that the pairwise application of CLIP fails to capture joint information between modalities, thereby limiting the quality of the learned representations. To address this issue, we present Symile, a simple contrastive learning approach that captures higher-order information between any number of modalities. Symile provides a flexible, architecture-agnostic objective for learning modality-specific representations. To develop Symile's objective, we derive a lower bound on total correlation, and show that Symile representations for any set of modalities form a sufficient statistic for predicting the remaining modalities. Symile outperforms pairwise CLIP, even with modalities missing in the data, on cross-modal classification and retrieval across several experiments including on an original multilingual dataset of 33M image, text and audio samples and a clinical dataset of chest X-rays, electrocardiograms, and laboratory measurements. All datasets and code used in this work are publicly available. 

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