Dissecting Representation Misalignment in Contrastive Learning via Influence Function

• URL: http://arxiv.org/abs/2411.11667v2
• Date: Fri, 31 Jan 2025 23:36:04 GMT
• Title: Dissecting Representation Misalignment in Contrastive Learning via Influence Function
• Authors: Lijie Hu, Chenyang Ren, Huanyi Xie, Khouloud Saadi, Shu Yang, Zhen Tan, Jingfeng Zhang, Di Wang,
• Abstract summary: We introduce the Extended Influence Function for Contrastive Loss (ECIF), an influence function crafted for contrastive loss.<n>ECIF considers both positive and negative samples and provides a closed-form approximation of contrastive learning models.<n>Building upon ECIF, we develop a series of algorithms for data evaluation, misalignment detection, and misprediction trace-back tasks.
• Score: 15.28417468377201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrastive learning, commonly applied in large-scale multimodal models, often relies on data from diverse and often unreliable sources, which can include misaligned or mislabeled text-image pairs. This frequently leads to robustness issues and hallucinations, ultimately causing performance degradation. Data valuation is an efficient way to detect and trace these misalignments. Nevertheless, existing methods are computationally expensive for large-scale models. Although computationally efficient, classical influence functions are inadequate for contrastive learning models, as they were initially designed for pointwise loss. Furthermore, contrastive learning involves minimizing the distance between positive sample modalities while maximizing the distance between negative sample modalities. This necessitates evaluating the influence of samples from both perspectives. To tackle these challenges, we introduce the Extended Influence Function for Contrastive Loss (ECIF), an influence function crafted for contrastive loss. ECIF considers both positive and negative samples and provides a closed-form approximation of contrastive learning models, eliminating the need for retraining. Building upon ECIF, we develop a series of algorithms for data evaluation, misalignment detection, and misprediction trace-back tasks. Experimental results demonstrate our ECIF advances the transparency and interpretability of CLIP-style embedding models by offering a more accurate assessment of data impact and model alignment compared to traditional baseline methods.

Related papers

• Impact of Missing Values in Machine Learning: A Comprehensive Analysis [0.0]
  This paper aims to examine the nuanced impact of missing values on machine learning (ML) models. Our analysis focuses on the challenges posed by missing values, including biased inferences, reduced predictive power, and increased computational burdens. The study employs case studies and real-world examples to illustrate the practical implications of addressing missing values.
  arXiv  Detail & Related papers  (2024-10-10T18:31:44Z)
• Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration [74.09687562334682]
  We introduce a novel training data attribution method called Debias and Denoise Attribution (DDA) Our method significantly outperforms existing approaches, achieving an averaged AUC of 91.64%. DDA exhibits strong generality and scalability across various sources and different-scale models like LLaMA2, QWEN2, and Mistral.
  arXiv  Detail & Related papers  (2024-10-02T07:14:26Z)
• A Gradient Analysis Framework for Rewarding Good and Penalizing Bad Examples in Language Models [63.949883238901414]
  We present a unique angle of gradient analysis of loss functions that simultaneously reward good examples and penalize bad ones in LMs. We find that ExMATE serves as a superior surrogate for MLE, and that combining DPO with ExMATE instead of MLE further enhances both the statistical (5-7%) and generative (+18% win rate) performance.
  arXiv  Detail & Related papers  (2024-08-29T17:46:18Z)
• On Effects of Steering Latent Representation for Large Language Model Unlearning [4.058064008234271]
  Representation Misdirection for Unlearning (RMU) is an effective method for large language model (LLM) unlearning. We show that steering forget representations in the intermediate layer reduces token confidence, causing LLMs to generate wrong or nonsense responses. We propose Adaptive RMU -- a simple yet effective alternative method that makes unlearning effective with most layers.
  arXiv  Detail & Related papers  (2024-08-12T15:24:50Z)
• Large Language Models are Biased Reinforcement Learners [0.0]
  We show that large language models (LLMs) exhibit behavioral signatures of a relative value bias. Computational cognitive modeling reveals that LLM behavior is well-described by a simple RL algorithm.
  arXiv  Detail & Related papers  (2024-05-19T01:43:52Z)
• Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models [36.05242956018461]
  In this paper, we establish a bridge between identifying detrimental training samples via influence functions and outlier gradient detection. We first validate the hypothesis of our proposed outlier gradient analysis approach on synthetic datasets. We then demonstrate its effectiveness in detecting mislabeled samples in vision models and selecting data samples for improving performance of natural language processing transformer models.
  arXiv  Detail & Related papers  (2024-05-06T21:34:46Z)
• The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes [30.30769701138665]
  We introduce and explore the Mirrored Influence Hypothesis, highlighting a reciprocal nature of influence between training and test data. Specifically, it suggests that evaluating the influence of training data on test predictions can be reformulated as an equivalent, yet inverse problem. We introduce a new method for estimating the influence of training data, which requires calculating gradients for specific test samples, paired with a forward pass for each training point.
  arXiv  Detail & Related papers  (2024-02-14T03:43:05Z)
• Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
  Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
  arXiv  Detail & Related papers  (2024-01-18T08:12:23Z)
• Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective [7.577040836988683]
  Missing data can pose a challenge for machine learning (ML) modeling. Current approaches are categorized into feature imputation and label prediction. This study proposes a Contrastive Learning framework to model observed data with missing values.
  arXiv  Detail & Related papers  (2023-09-18T13:16:24Z)
• Stubborn Lexical Bias in Data and Models [50.79738900885665]
  We use a new statistical method to examine whether spurious patterns in data appear in models trained on the data. We apply an optimization approach to *reweight* the training data, reducing thousands of spurious correlations. Surprisingly, though this method can successfully reduce lexical biases in the training data, we still find strong evidence of corresponding bias in the trained models.
  arXiv  Detail & Related papers  (2023-06-03T20:12:27Z)
• To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
  Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs. We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting. Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
  arXiv  Detail & Related papers  (2023-05-22T17:02:15Z)
• Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation [24.794022951873156]
  We propose to learn Prototypical representation through Alignment, Uniformity and Correlation (PAUC) Specifically, the ordinary ProtoNCE loss is revised with: (1) an alignment loss that pulls embeddings from positive prototypes together; (2) a loss that distributes the prototypical level features uniformly; (3) a correlation loss that increases the diversity and discriminability between prototypical level features.
  arXiv  Detail & Related papers  (2022-10-18T22:33:12Z)
• Practical Insights of Repairing Model Problems on Image Classification [3.2932371462787513]
  Additional training of a deep learning model can cause negative effects on the results, turning an initially positive sample into a negative one (degradation) In this talk, we will present implications derived from a comparison of methods for reducing degradation. The results imply that a practitioner should care about better method continuously considering dataset availability and life cycle of an AI system.
  arXiv  Detail & Related papers  (2022-05-14T19:28:55Z)
• On Modality Bias Recognition and Reduction [70.69194431713825]
  We study the modality bias problem in the context of multi-modal classification. We propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned. Our method yields remarkable performance improvements compared with the baselines.
  arXiv  Detail & Related papers  (2022-02-25T13:47:09Z)
• MAML is a Noisy Contrastive Learner [72.04430033118426]
  Model-agnostic meta-learning (MAML) is one of the most popular and widely-adopted meta-learning algorithms nowadays. We provide a new perspective to the working mechanism of MAML and discover that: MAML is analogous to a meta-learner using a supervised contrastive objective function. We propose a simple but effective technique, zeroing trick, to alleviate such interference.
  arXiv  Detail & Related papers  (2021-06-29T12:52:26Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• Exploring Adversarial Robustness of Deep Metric Learning [25.12224002984514]
  DML uses deep neural architectures to learn semantic embeddings of the input. We tackle the primary challenge of the metric losses being dependent on the samples in a mini-batch. Using experiments on three commonly-used DML datasets, we demonstrate 5-76 fold increases in adversarial accuracy.
  arXiv  Detail & Related papers  (2021-02-14T23:18:12Z)
• Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning [94.35586521144117]
  We investigate whether applying contrastive learning to fine-tuning would bring further benefits. We propose Contrast-regularized tuning (Core-tuning), a novel approach for fine-tuning contrastive self-supervised visual models.
  arXiv  Detail & Related papers  (2021-02-12T16:31:24Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Influence Functions in Deep Learning Are Fragile [52.31375893260445]
  influence functions approximate the effect of samples in test-time predictions. influence estimates are fairly accurate for shallow networks. Hessian regularization is important to get highquality influence estimates.
  arXiv  Detail & Related papers  (2020-06-25T18:25:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.