Linear probing deep learning pdf . GPs are a well-studied and powerful proba ilistic tool in machine learning [54]. 3. Learn about the construction, utilization, and insights gained from linear probes, alongside their limitations and challenges. We also demonstrate that BFP helps learn better representation space, in which linear separability is well preserved during continual learning and linear prob-ing achieves high classification accuracy. near layer as the probing classifier. Linear-Probe Classification: A Deep Dive into FILIP and SODA | SERP AIhome / posts / linear probe classification When running a linear probe on ImageNet, we follow recent literature and use SGD with momentum 0:9 and a high learning rate (we try the values 30, 10, 3, in the manner described above) (He et al. , 2019). Ever since the early successes of deep reinforcement learning [36], neural networks have been widely adopted to solve pixel-based reinforcement learning tasks such as arcade games [6], physical Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. org e-Print archive But also real-world Machine-Learning problems are often formulated as linear equations and inequalities Either because they indeed are linear Or because it is unclear how to represent them and linear is an intuitive compromise A stepping stone for solving more complicated nonlinear optimization problems, which you would see later. LG] 21 Feb 2022 Then, we use the result-ing models in transfer toward six diversified downstream tasks using linear probing and full fine tuning for down-stream training. Recently, linear probes [3] have been used to evalu-ate feature generalization in self-supervised visual represen-tation learning. Results linear probe scores are provided in Table 3 and plotted in Figure 10. In this work, we propose and exam-ine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear uate the model on kNN, linear-probing, few-shot learning, parameter-eficient fine-tuning, and end-to-end fine-tuning scenari s. In linear probing, only the linear readout head is trained on the new task, while the weights of all other layers in the model are frozen at their initial (pretrained) values. However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of We evaluate the trained representations by linear probing on the clean testing data. Fine-tuning updates all the parameters of the model. EnhancingIn-contextLearningviaLinearProbeCalibration Enhancing In-context Learning via Linear Probe Calibration We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. Left: Fine-tuning pre-trained ViT significantly outper-forms training Wide ResNet starting from scratch. Analyzing Linear Probing When looking at k-independent hash functions, the analysis of linear probing gets significantly more complex. 0}, and the best linear probing accuracy under the optimal θ is reported. In all, this work provides the first provable analysis for contrastive learning where guarantees for linear probe evaluation can apply to realistic empirical settings. Initially, linear probing (LP) optimizes only the linear head of the model, after which fine-tuning (FT) updates the entire model, including the feature extractor and the linear head. This paper proposes a new federated learning method called FedLP + FT. For example, MAE opens the door for sparse pre-training of Vision Transformers (ViTs) [23] by masking large parts of the image and not processing the masked areas. Using a neighboring word identity prediction task, we show that the token embeddings learned by neural sentence encoders contain a significant amount of information about the exact linear context of the token, and hypothesize that, with such information, learning standard probing tasks may be feasible even without additional linguistic structure. 4, 0. An innovative aspect of our methodology was the development of a specialized loss function. Our exploration into fine-tuning methods, including traditional fine-tuning, linear probing, and their combination, revealed traditional fine-tuning as the superior approach for our use case, as detailed in Table 3. Nov 19, 2022 · We have integrated all SSL codebases into one reusable library called cxrlearn. 2, 0. Consequently, the model may not adapt as well to the ike linear probing can improve out-of-distribution (OOD) performance. Hou et al. However, linear probing tends to have an unsatisfactory performance and misses the opportunity of pursuing strong but non-linear features [43], which indeed benefit deep learning. This OOD gap be-tween fine-tuning and linear probing grows as the quality of pretrained features improve, so we believe our results are likely to gain sig Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. In proto-type probing, the probing classifier is a linear layer whose weight matrix is cal Nov 21, 2025 · In the linear probe setting, MGLL achieves significant advancements over the second-best method (UniChest Dai et al. ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias towards structured probes thus reducing Sep 19, 2024 · Linear Probing is a learning technique to assess the information content in the representation layer of a neural network. This linear probe does not affect the training procedure of the model. Linear probing is a straightforward approach to maintaining the pre-trained model fixed by only tuning a specific lightweight classification head for every task. I've successfully made a spell checker using one. Jan 28, 2025 · We notice that the two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), performs well in centralized transfer learning, so this paper expands it to federated learning problems. 10054v1 [cs. Moreover, these probes cannot affect the training phase of a A source of valuable insights, but we need to proceed with caution: É A very powerful probe might lead you to see things that aren’t in the target model (but rather in your probe). In the popular linear probing protocol, a linear readout functions φj is used to assess the quality of f. We examine several contrastive learning methods [6–9, 28, 59, 69] and observe a negative corre-lation between ImageNet linear probing accuracy and task perfor-mance. (2024) on MIDRC-XR-Portable), with improve-ments of 2. The weight parameter θ of JointTrain-ing is tuned in {0, 0. However, we discover that curre t probe learning strategies are ineffective. Indeed representation learning is at the core of deep learning meth-ods in supervised and unsupervised settings [10]. While simple, we demonstrate it greatly enhances probing methods, and also outperforms other Mid-Level Vision v. 23% and 3. (2019) show that utilizing cosine linear layers mit- gates bias towards new classes in IL. Jul 18, 2024 · aluation protocols for self-supervised learning in a selection of papers. Jun 5, 2023 · Learning Transferable Visual Models From Natural Language Supervision. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to probing approaches. by this work. Experimental results confirm previous ones regarding performance saturation in downstream tasks, but we find that saturation occurs faster for compact deep ar-chitectures. For organ segmentation tasks, we compare lightweight and non-lightweight decoders, Download scientific diagram | General framework of our analysis approach: linear probing of representations from pre-trained SSL models on EMA from publication: Evidence of Vocal Tract We demonstrate that combining low-rank adaptation with linear probing of foundation models yields exceptional segmentation performance while main-taining parameter efficiency. However, despite the widespread use of Figure 1: Linear probing state-of-the-art on ImageNet-1K over the last four years. Typically, a task is designed to verify whether the representation contains the knowledge of a specific interest. Where we're going: Theorem:Using 2-independent hash functions, we can prove an O(n1/2) expected cost of lookups with linear probing, and there's a matching adversarial lower bound. Oct 14, 2024 · We propose Deep Linear Probe Gen erators (ProbeGen) for learning better probes. In contrast, linear probing requires less computational resources but offers less flexibility since only the last layer is adjusted. 10 FID in the task of class-unconditional image generation and 78. 1, 0. This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. Specifically, the logits are computed as the cosine similarities between clas ifier weights and extracted features. Moreover, these probes cannot affect the training phase of a model, and they are generally added after training. Empirically, the features learned by our objective can match or outperform several strong baselines on benchmark vision datasets. included in the Cloppe 4 days ago · Abstract: Neural network models have a reputation for being black boxes. Linear probing definitely gives you a fair amount of signal. Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. The success of MIM is driven by methods like Masked Autoencoder (MAE) [31], data2vec [7, 6], and others [8, 90]. This method has been extensively analyzed and enhanced [50, 46, 16, 26]. This tutorial showcases how to use linear classifiers to interpret the representation encoded in different layers of a deep neural network. These networks remain highly accurate while also being more amenable to human interpretation, as we demon-strate quantitatively via numerical and human ex-periments. Right: VPT improves full fine-tuning and linear probing by a large margin. We further identify that linear probing excels in preserving robustness from the ro-bust pretraining. Linear probing freezes the foundation model and trains a head on top. Dec 7, 2023 · This paper introduces LiDAR (Linear Discriminant Analysis Rank), a metric designed to measure the quality of representations within JE architectures, and empirically demonstrates that LiDar significantly surpasses naive rank based approaches in its predictive power of optimal hyperparameters. Our extensive ablation studies validate this ap-proach as both computationally lightweight and highly effective for historical document analysis. A. 8, 1. Linear mode connectivity and git rebasin. Deep learning has been traditionally motivated as an ap-proach, which can automatically learn representations [4], forgoing the need to design handcrafted features. Final section: unsupervised probes. 1 Transfer learning Full fine-tuning requires more computational resources but usually achieves better results because it allows updating the model’s understanding of both low-level and high-level features. Results show that the bias towards simple solutions of generalizing networks is maintained even when statistical irregularities are intentionally introduced. There exists an interesting corre-spondence among linear regression, Bayesian linear regres-sion, and GP regression, with the last o Feb 21, 2022 · When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer -- the "head"). We propose a new method to understand better the Oct 14, 2024 · However, we discover that current probe learning strategies are ineffective. It then observes the responses from all probes, and trains an MLP classifier on them. However, despite the widespread use of Our method uses linear classifiers, referred to as “probes”, where a probe can only use the hidden units of a given intermediate layer as discriminating features. A denotes that the protocol was used in the mentioned paper. 2 Linear classifier probes Linear Probes (LP) are classifiers (such as Multi-Layer Perceptrons, MLPs) that contribute to deep learning models explainability eforts by providing insights into how the model processes information internally [2]. ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias towards structured probes thus reducing For in-stance, in (Alain & Bengio, 2017), it was demonstrated that linear probing of intermediate layers in a trained network becomes more accurate as we move deeper into the network. ProbeGen optimizes a deep generator module limited to linear expressivity, that shares information between the different probes. Jun 1, 2024 · Their fine-tuning strategy consisted of first training only the last classification layer (linear probing) and then fine-tuning some of the CNN layers with a smaller learning rate. Self-supervised learning methods, particularly contrastive learning (CL), have proven successful by leveraging data augmentations to define positive pairs. ImageNet Linear Probing with Contrastive Learning Methods. 81% in AUC, respectively, indicating superior representation learning capa-bilities. Probes in the above sense are supervised Apr 1, 2017 · Request PDF | Understanding intermediate layers using linear classifier probes | Neural network models have a reputation for being black boxes. Contribute to jonkahana/ProbeGen development by creating an account on GitHub. 1. However, for functions for which the input data are linear operators, vectorizing the input destroys the un-derlying operator structure. How do I compare the performance of linear probing vs separate chaining (for hash table) in my code? My textbook provides two classes, one for linear probing and one for separate chaining. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head during LP. s. e. However, recent studies have This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. On ImageNet-1K, a single MAGE ViT-L model obtains 9. Joint embedding (JE) architectures have emerged as a promising avenue for acquiring transferable data Jun 1, 2022 · Prior work on understanding catastrophic forgetting in deep learning for image classification used representational analysis techniques such as centered kernel alignment [26] and linear probing to Ex-tensive experiments show that our UniCL is an effective way of learning semantically rich yet discriminative repre-sentations, universally for image recognition in zero-shot, linear-probing, fully finetuning and transfer learning sce-narios. Ultrasound Knbology, Ultrasound Probes/Transducers, and Ultrasound Modes made EASY! Linear probing has been a popular protocol in the past few years; however, it misses the oppor-tunity of pursuing strong but non-linear features—which is indeed a strength of deep learning. Abstract Learning useful data representations without requiring labels is a cornerstone of modern deep learning. 1 Introduction In standard deep learning, the input data are represented by vectors, and each layer of a deep neural network applies an a ne transformation (a matrix-vector product plus a shift) composed with nonlinear activation functions. É Probes cannot tell us about whether the information that we identify has any causal relationship with the target model’s behavior. The linear classifier as described in chapter II are used as linear probe to determine the depth of the deep learning network as shown in figure 6. We study that in pretrained networks trained on ImageNet. This success has prompted a number of theoretical studies to better understand CL and investigate theoretical bounds for Apr 24, 2023 · Logit lens works. PALP inherits the scalabil- ity of linear probing and the capability of enforcing language models to derive more meaningful representations via tailor- ing input into a more conceivable form. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based This technique involves the integration of a linear probing layer, meticulously trained using pseudo annotations generated through a consistency learning mechanism extracted from CLIP. It is well known that fine-tuning leads to better accuracy in-distribution (ID). INTRODUCTION Despite recent advances in deep learning, each intermediate repre-sentation remains elusive due to its black-box nature. 2. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. You can merge together different models finetuned from the same initialization. Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8748-8763, 2021. Our work does not aim to propose a new distillation ap-proach, but leverage this flexible framework with crucial designs to mitigate the differences on input ratios and tar-get granularity between CLIP and MIM methods. We propose a new method to better understand the roles and dynamics of the intermediate layers. knn: k-nearest neighbors probing, LP: linear probing, FT: fine-tuning, FSFT: few-shot fine-tuning (1% or Apr 4, 2023 · Linear probing definitely gives you a fair amount of signal Linear mode connectivity and git rebasin Colin Burns’ unsupervised linear probing method works even for semantic features like ‘truth’ You can merge together different models finetuned from the same initialization You can do a moving average over model checkpoints and this is better! Our method uses linear classifiers, referred to as “probes”, where a probe can only use the hidden units of a given intermediate layer as discriminating features. Abstract We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable deep networks. We cannot directly ask the pretrained network Apr 5, 2023 · Two standard approaches to using these foundation models are linear probing and fine-tuning. , 2016]. My next step for extra credit is to implement the other and compare/describe performance differences. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective mod-ification to probing approaches. 4 show that CF-SimCLR consistently outperforms standard SimCLR across all ID scanners, in all scenarios with up to 20k labelled images. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. ferent properties of the pre-training. The basic idea is simple — a classifier is trained to predict some linguistic property from a model’s representations — and has been used to examine a wide variety of models and properties. This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. Colin Burns' unsupervised linear probing method works even for semantic features like 'truth'. Figure 1. Ex-periments are conducted on CIFAR-100 using FixMatch. ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias 1 Introduction Learning visual representations is a critical step towards solving many kinds of tasks, from supervised tasks such as image classification or object detection, to reinforcement learning. 1 Linear Classifier Probing Probe technology (Alain and Bengio, 2016) is a method for analyzing and evaluating the internal representations of a neural network by applying Sep 13, 2024 · This paper introduces Kolmogorov-Arnold Networks (KAN) as an en-hancement to the traditional linear probing method in transfer learning. (2024) on MIDRC-XR and UniMed-CLIP Khattak et al. Index Terms—representation engineering, probing classifiers, chess, language models, GPT We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. Dec 10, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and e fective mod-ification to probing approaches. Which method does better? deep-learning recurrent-networks linear-probing curriculum-learning energy-based-model self-supervised-learning spatial-embeddings vicreg jepa world-model joint-embedding-prediction-architecture agent-trajectory latent-prediction Sep 13, 2024 · This paper introduces Kolmogorov-Arnold Networks (KAN) as an en-hancement to the traditional linear probing method in transfer learning. 6, 0. The linear probe is a linear classifier taking layer activations as inputs and measuring the discriminability of the networks. Deep Gaussian processes. This holds true for both indistribution (ID) and out-of-distribution (OOD) data. g. Based on this, we propose Robust Linear Initialization (RoLI) for adversarial finetuning, which ini-tializes the linear head with the weights obtained by ad-versarial linear probing to maximally inherit the robust-ness from pretraining. arXiv:2202. widely used in deep reinforcement learning [48, 4], life-long learning [62] and recommendation system [7]. This method is very fast and eficient in terms of the number of parameters trained, but it can be suboptimal due to its low capacity to BFP can be integrated with existing experience replay methods and boost performance by a sig-nificant margin. Mainstream eSSLs are bench-marked with linear probing by freezing ECG en-coders and fine-tuning a linear layer on 1%, 10%, and 100% of labeled data from the six datasets. Using Deep Networks–parameterized non-linear operators –to map observations to embeddings is a standard first piece of that puzzle [LeCun et al. However, despite the widespread use of large language We extensively evaluate the generation and representation learning capabilities of MAGE. Linear probing, often applied to the final layer of pre-trained models, is limited by its inability to model complex relationships in data. However, recent studies have a probing baseline worked surprisingly well. On mammography, linear probing results in Fig. However, despite the widespread use of large Jun 8, 2021 · In all, this work provides the first provable analysis for contrastive learning where guarantees for linear probe evaluation can apply to realistic empirical settings. 05, 0. In this paper, we probe the activations of intermediate layers with linear classification and regression. ProbeGen op-timizes a deep generator module limited to linear expressivity, that shares information between the different probes. Solving the task well implies that the However, we discover that current probe learning strategies are ineffective. Functions whose The most common approaches for transfer learning are linear probing and finetuning. Learn the Basics of Ultrasound Machine Settings. Oct 25, 2024 · This guide explores how adding a simple linear classifier to intermediate layers can reveal the encoded information and features critical for various tasks. SVD directions. The second, less standardized, An official implementation of ProbeGen. The computational eficiency, coupled Nov 16, 2021 · We have developed a deep learning framework, StructureImpute, to infer RNA structure scores for nucleotides with missing values in the results of an RNA structural probing experiment (Methods). arXiv. We further illustrate how the resulting sparse explanations can help to identify spurious Oct 5, 2016 · View a PDF of the paper titled Understanding intermediate layers using linear classifier probes, by Guillaume Alain and Yoshua Bengio Apr 4, 2022 · Abstract. This document is part of the arXiv. They share a deep connection with neural networks NNs) with infinite width [24, 31, 37]. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Through-out the paper, we denote the setting with 4 labeled samples for each class as “N4”, and other settings are defined accordingly. This is done to answer questions like what property of the data in training did this representation layer learn that will be used in the subsequent layers to make a prediction. Linear probing is a tool that enables us to observe what information each representa-tion contains [1,2]. 9% top-1 accuracy for linear probing, achieving state-of-the-art performance in both image generation and representation learning. The basic idea is simple—a classifier is trained to predict some linguistic property from a model’s representations—and has been used to examine a wide variety of models and properties. For the remainder of the paper we restrict our investigations to linear probing. Our method uses linear classifiers, referred to as "probes", where a probe can only use the hidden units of a given intermediate layer as discriminating features. linear probe) but modify the data distribution by changing the encoder. , 2015, Goodfellow et al. The best-performing CLIP model, using ViT-L/14 archiecture and 336-by-336 pixel images, achieved the state of the art in 21 of the 27 datasets, i. The weights of the learned linear classifiers are very informative and can be used to reliably delete pieces from the board showing that the model internally maintains an editable emergent representation of game state. Abstract In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often re-ported as a weak baseline. Abstract This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. cxrlearn provides documented functions for dataset conversion, self-supervised learning, model object generation, finetuning, linear probing, and evaluation. To assess whether a certain feature is encoded in the representation learnt by a network, we can check its discrimination power for that said feature. The usability-probe generalization tradeoff then corresponds to the probe’s supervised tradeoff if we keep the probing family fixed (e. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. They allow us to understand if the numeric representation 1) visual prompting methods (VP, VPT, EVP) consistently outperform linear probing, demonstrating the effectiveness of visual prompting in learning with limited labeled data; and 2) among all visual prompting methods, EVP consis-tently achieves the best overall performance, demonstrating its strong generalization ability at different data scale. org e-Print archive, providing access to scientific research papers across various disciplines. 4. Linear probing, which learns a batch normalization and linear layer on top of frozen features, tests the utility of the learned feature representations – it shows whether the pre-training learns disentangled representations, and whether these fe We further identify that linear probing excels in preserving robustness from the ro-bust pretraining. ayvxc wublu yeojguxu fxtnxjdr bcap vyruz djeo mbw ilj vdyvs mpvn bewp rqd antah ycj