: Linear probing fine tuning machine learning In the ID setting it is well known that fine-tuning leads to better accuracy than linear probing (Kornblith et al. However, one of the most commonly used methods, linear probing, which involves training a linear classifier on top of the frozen features from the Talk in Workshop: Transfer Learning for Natural Language Processing Fine-Tuning without Distortion: Improving Robustness to Distribution Shifts Percy Liang · Ananya Kumar [ Abstract ] 2022 Talk 1st Linear probing (LP), 2nd Fine-tuning (FT) FT starts with the optimized linear layer (classifier). However, despite the widespread use of Mar 23, 2023 · Advances in the expressivity of pretrained models have increased interest in the design of adaptation protocols which enable safe and effective transfer learning. By probing a pre-trained model's internal representations, researchers and data Poster Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective Akiyoshi Tomihari · Issei Sato Jun 17, 2024 · We evaluated eight fine-tuning strategies, including standard techniques such as fine-tuning all layers or fine-tuning only the classifier layers, alongside methods such as gradually unfreezing layers, regularization based fine-tuning and adaptive learning rates. , the failure to update features orthogonal to the in-distribution, have been found to Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. 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"). Abstract—Based on the success of large-scale visual foundation models like CLIP in various downstream tasks, this paper initially attempts to explore their impact on Long-Tailed Semi-Supervised Learning (LTSSL) by employing the foundation model with three strategies: Linear Probing (LP), Lightweight Fine-Tuning (LFT) and Full Fine-Tuning (FFT). ID vs. To motivate our approach, we first find that visual prompt tuning (VPT) (Jia et al. A natural question is whether this phenomenon is generally true for DP machine learning, and if so, how much linear probing is enough? In other words, given a total privacy budget, how much should we allocate to linear probing vs. Changes to pre-trained features are minimized. 4). Our analysis presents the following insights: i Transfer learning has become a cornerstone of modern machine learning, particularly in scenarios with limited labeled data [1]. However, despite the widespread use of . Jan 1, 2022 · Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning heuristic, combines the benefits of both fine-tuning and linear probing. Apr 4, 2022 · Abstract. This paper proposes a new federated learning method called FedLP FT. However, despite the Abstract Recently, eficient fine-tuning of large-scale pre-trained models has attracted increasing research interests, where linear probing (LP) as a fundamental module is involved in exploiting the final representations for task-dependent classification. However, the ICL performance does not scale well with the number of available training samples as it is limited by the inherent input length constraint of the underlying language model. However, despite the widespread use of large Dec 30, 2024 · Different methods to save pretrained models. Oct 23, 2024 · They show that linear probing creates an improved initialization state for fine-tuning. The two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), consistently outperforms linear probing (LP) and FT alone in terms of accuracy for both in-distribution (ID) and out-of-distribution (OOD) data. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. machine-learning computer-vision deep-learning master-thesis transformers pytorch image-classification transfer-learning linear-probing fine-tuning huggingface vision-transformers zero-shot-transfer prompt-engineering May 13, 2022 · First, we compare the two popular update methods, full fine-tuning (i. Jan 14, 2025 · In this paper, we exploit models obtained in Self-Supervised Learning (SSL) to mitigate the impact of noisy labels in FL. However, the potential of foundation models in improving SSL remains unexplored. Looking at the request Sep 13, 2024 · This paper introduces Kolmogorov-Arnold Networks (KAN) as an en-hancement to the traditional linear probing method in transfer learning. Key architectural insights include the importance of maintaining the probing head during fine-tuning and the role of learning rate scheduling between the two phases. However, despite the widespread use of 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"). This method has been extensively analyzed and enhanced [50, 46, 16, 26]. It is well known that fine-tuning leads to better accuracy in-distribution (ID). Robustness to distribution shifts Train Pedestrians using a crosswalk A core challenge for reliable machine learning in the wild Feb 29, 2024 · This paper (1) analyzes the training dynamics of DP linear probing (LP) and full fine-tuning (FT), and (2) explores the phenomenon of sequential fine-tuning, starting with linear probing and transitioning to full fine-tuning (LP-FT), and its impact on test loss. The theoretical results are supported by empirical evaluations on various benchmarks and models. OOD:99981231160000-0800 different directions, not just reweighting Pretrained Features Fine-tuning: features for ID examples change in sync with the linear head Feature distortion Head performs poorly on OOD examples Features for OOD examples change less ID OOD Pretrained Features Fine-tuning Linear probing: freezes pretrained features Head performs poorly on OOD examples Pretrained Aug 17, 2025 · This paper (1) analyzes the training dynamics of DP linear probing (LP) and full fine-tuning (FT), and (2) explores the phenomenon of sequential fine-tuning, starting with linear probing and transitioning to full fine-tuning (LP-FT), and its impact on test loss. •Prior work studies linear probing (fitting linear head on features) •Fine-tuning is non-convex, trajectory is complicated and has no known closed form even for two-layer linear networks •Tool: leverage invariants that hold throughout process of fine-tuning In this paper, we exploit models obtained in Self-Supervised Learning (SSL) to mitigate the impact of noisy labels in FL. However, despite the widespread use of large Apr 5, 2023 · Two standard approaches to using these foundation models are linear probing and fine-tuning. However, despite the Using probes, machine learning researchers gained a better understanding of the difference between models and between the various layers of a single model. We evaluated eight fine-tuning strategies, including standard techniques such as fine-tuning all layers or fine-tuning only the classifier layers, alongside methods such as gradually unfreezing layers, regularization based fine-tuning and adaptive learning rates. However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of Abstract This work explores the comparative eficacy of full fine-tuning, linear probing, and Parameter-Eficient Fine-Tuning (PEFT) techniques, with a focus on Low-rank adaptation (LoRA), in training models for natural language processing tasks such as sentiment classification, paraphrase detection, and semantic textual similarity. Dec 21, 2022 · Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning. This success is largely attributed to the preservation of pre-trained features, achieved through a near-optimal linear head obtained during LP. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. , updating the entire network, FT) and linear probing (i. The technique of linear probing, where pretrained features are used for lightweight Jul 30, 2023 · Despite the fact that MIM models show good performance on fine-tuning and transfer learning, the linear probing accuracy of these approaches is worse than that of contrastive learning. Dec 29, 2024 · Start Simple: Linear probing often works surprisingly well with just one or two layers on top. In this section, we'll cover the options Ludwig offers for fine-tuning, and provide guidance on when to use which techniques depending on your task. However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of Jan 9, 2023 · An overview of 'fine-tuning' - the practice of taking existing trained machine learning models and continuing to train them on new data. However, despite the widespread use of In this paper, we exploit models obtained in Self-Supervised Learning (SSL) to mitigate the impact of noisy labels in FL. We notice that the two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), performs well in central-ized transfer learning, so this paper expands it to federated learning problems. , 2019; Zhai et al. , 2022), a representative parameter-eficient fine-tuning (PEFT) method, is better suited for SSL tasks compared to commonly used full fine-tuning (FFT) and linear probing (LP). Linear probing Full fine-tuning Epochs of fine-tuning Theory says fine-tuning does worse than linear probing if features good, distribution shift large arXiv:2202. However, exist-ing work primarily adopts either full parameter fine-tuning or simple linear probing techniques when applying these models to new tasks. Oct 17, 2025 · Original Source Title: Tuning Pre-trained Model via Moment Probing Abstract: Recently, efficient fine-tuning of large-scale pre-trained models has attracted increasing research interests, where linear probing (LP) as a fundamental module is involved in exploiting the final representations for task-dependent classification. Comparison with supervised models: CLIP is always more computationally efficient → best gain with scaling. Related to finetuning in the field of training Foundation models is linear probing Jan 28, 2025 · This paper trys to demonstrate through an intuitive approach that training the classification head using linear probing is better than directly fine-tuning the entire model in federated learning. Linear-Probe Classification: A Deep Dive into FILIP and SODA | SERP AIhome / posts / linear probe classification Linear probing: evaluating representation learning with linear classifiers instead of end-to-end fine tuning (expensive, many params, masks failures). May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), consistently outperforms linear probing (LP) and FT alone in terms of accuracy for both in-distribution (ID) and out-of-distribution (OOD) data. However, recent studies have Dec 10, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. , 2020), and even when testing OOD Features change orders of magnitude less with LP-FT LP-FT Early stopping does not solve the problem with fine-tuning OOD Acc. Aug 6, 2025 · Title: Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective Abstract: The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. 10054v1 [cs. Linear probing is a technique where you take the second-to-last layer of a NN (so the layer before the output layer) and further tune the weights from the base model using your datasets. When to Use Linear Probing Your dataset is small, and full fine-tuning might lead to overfitting. 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. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head during LP. Meanwhile, many studies have revealed that language models are also powerful We highlight the limitations of current fine-tuning methods and the challenges of learning ro-bust models. These results contribute to a deeper understanding of DP machine learning and highlight the importance of considering the allocation of privacy budget in the fine-tuning process. Going beyond conventional linear probing (LP) and fine tuning (FT) strategies, protocols that can effectively control feature distortion, i. Linear probing, often applied to the final layer of pre-trained models, is limited by its inability to model complex relationships in data. full fine-tuning to achieve the best test accuracy? These questions are currently dificult to answer because we lack a theoretical understanding of the impact of DP This paper (1) analyzes the training dynamics of DP linear probing (LP) and full fine-tuning (FT), and (2) explores the phenomenon of sequential fine-tuning, starting with linear probing and transitioning to full fine-tuning (LP-FT), and its impact on test loss. , 2020; He et al. , updating only a linear classifier, LP). 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. Sep 5, 2024 · In this paper, we exploit models obtained in Self-Supervised Learning (SSL) to mitigate the impact of noisy labels in FL. Nov 28, 2022 · I’m not an expert, so please take this with a grain of salt, but based on my experience working with OpenAI’s CLIP, fine-tuning pre-trained OpenAI models works via linear probing. May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. This holds true for both indistribution (ID) and out-of-distribution (OOD) data. e. These classifiers aim to understand how a model processes and encodes different aspects of input data, such as syntax, semantics, and other linguistic features. The study introduces MultitaskBERT, a model leveraging the BERT After initializing with a pretrained model, two popular transfer methods are fine-tuning (running gradient descent on all the model parameters), and linear probing (tuning the head but freezing lower layers). One key reason for its success is the preservation of pre-trained features, achieved by Finetuning # Fine-tuning refers to a process in machine learning where a pre-trained model is further trained on a specific dataset to adapt its parameters to a downstream task characterized by a relevent domain. Linear probing freezes the foundation model and trains a head on top. 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. May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. In addition, we explore two popular methods to transfer to downstream tasks: linear probing, which updates only the last classification layers, and fine-tuning, which updates all model parameters. Sep 25, 2024 · The authors present a theoretical analysis of the linear probing and fine-tuning framework based on neural tangent theory, supported by experiments with transformer-based models on natural language processing benchmarks. What are Probing Classifiers? Probing classifiers are a set of techniques used to analyze the internal representations learned by machine learning models. Mar 24, 2025 · These models aim to lever-age large-scale, unsupervised learning to capture general temporal patterns that can be fine-tuned for specific downstream applications. Experimental results confirm previous ones regarding performance saturation in downstream tasks, but we find that saturation occurs faster for compact deep ar-chitectures. LG] 21 Feb 2022 Jun 1, 2024 · We evaluated eight fine-tuning strategies, including standard techniques such as fine-tuning all layers or fine-tuning only the classifier layers, alongside methods such as gradually unfreezing layers, regularization based fine-tuning and adaptive learning rates. The findings reveal the complex nature of DP fine-tuning methods. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based This removes the need for vali-dation searches for the optimization hyper-parameters, re-ducing the computational load for fine-tuning (Table 2), while yielding performances on par with the best learning rates found with validation (Fig. machine-learning computer-vision deep-learning master-thesis transformers pytorch image-classification transfer-learning linear-probing fine-tuning huggingface vision-transformers zero-shot-transfer prompt-engineering Updated on Jun 15 Jupyter Notebook Model tuning is the process of optimizing a machine learning model’s hyperparameters to obtain the best training performance. It’s distinct from training a model from scratch using the downstream task dataset exclusively. However, despite the widespread use of large language Fine-tuning is the process of modifying the weights of a Large Language Model to help it perform better on a specific task or set of tasks. How to fine-tune models to fit specific datasets and tasks. The proposed method, named Weight-Space Ensem-bles for Fine-Tuning then Linear Probing (WiSE-FT-LP), integrates the original pre-training and fine-tuning models through weight space integration followed by Linear Probing. By leveraging pre-trained models such as ResNet-50 [2], transfer learning allows for efficient adaptation to new tasks. We find that LP is better than FT with extremely few samples, whereas FT outperforms LP as training samples increase.

Linear probing fine tuning machine learning. However, despite the widespread use of .