Linear probing paper Download Table | Hash table representation for linear probing from publication: An Efficient Strategy for Collision Resolution in Hash Tables | This paper presents NFO, a new and innovative Figure 1. May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. We give a uni ed analysis of linear probing hashing with a gen-eral bucket size. Dec 21, 2022 · This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. 0 12 4 13 14 11 1 2 3 10 11 10 0 1 2 3 4 5 6 7 9 8 10 11 12 13 14 15 The lookup time here is hugeeven though this key only directly collides with one other. MAGE achieves SOTA performance in linear probing and es-tablishes a new SOTA in class unconditional generation. ELP is trained with detached features from the network and re-initialized episodically. e. Sep 19, 2023 · This technique involves the distillation of CLIP features through the incorporation of a linear probing layer positioned atop its encoding structure. Our metric addresses several shortcomings of recent approaches based on feature covariance rank by discriminating between informative and uninforma- tive features. Oct 14, 2024 · However, we discover that current probe learning strategies are ineffective. A specific modeling of the classifier weights, blending visual prototypes and text embeddings via learnable multipliers, along with convex-optimization ingredients, often overlooked in deep learning practices, led to the surprising results. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to probing approaches. It is widely believed and taught, however, that linear probing should never be used at high load factors; this is because of an effect known as primary clustering Proposed: Few-shot adapters with model selection strategy based only on the support set. Moreover, these probes cannot affect the training phase of a model, and they are generally added after training. Can the authors provide some insights into how th Nov 24, 2022 · In this paper, we propose Transductive Linear Probing as an alternative approach for meta-learning to few-shot node classification. Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Sep 19, 2024 · Linear probing and non-linear probing are great ways to identify if certain properties are linearly separable in feature space, and they are good indicators that these information could be used for future token prediction. Recent work by Bender, Kuszmaul, and Kuszmaul (FOCS'21), however, has added a new twist to this story: in some versions of linear probing, if the \emph {maximum} load factor is at most 1 − 1/x, then the \emph {amortized} expected time per 4 days ago · 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. 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. Oct 23, 2024 · This framework explains why linear probing helps guide the subsequent fine-tuning process. One of the simple strategies is to utilize a linear probing classifier to quantitatively evaluate the class accuracy under the obtained features. This success is largely attributed to the preservation of pre-trained Sep 12, 2024 · This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. LG] 21 Feb 2022 Paper Includes standard full model, linear probing and parameter efficient strategies like Block Expansion and LoRA for fine-tuning Vision Transformers (ViTs) for image classification. Already Carter and Wegman, in their seminal paper on universal hashing, raised the question of extending their analysis to linear probing. 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). A typical approach for OVOD is to use joint text-image embeddings of CLIP to assign box proposals to their closest text label. Nov 7, 2025 · View a PDF of the paper titled From Linear Probing to Joint-Weighted Token Hierarchy: A Foundation Model Bridging Global and Cellular Representations in Biomarker Detection, by Jingsong Liu and 3 other authors Code of paper: Probing the Difficulty Perception Mechanism of Large Language Models - H123S456T/IF_llm_see_image_real Linear probing: evaluating representation learning with linear classifiers instead of end-to-end fine tuning (expensive, many params, masks failures). Comparison with supervised models: CLIP is always more computationally efficient → best gain with scaling. 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. This newly added layer is trained utilizing pseudo-labels produced by CLIP, coupled with a self-training strategy. This success is largely attributed to the preservation of pre-trained features, achieved through a near-optimal linear head obtained during LP. The recent Masked Image Modeling (MIM) approach is shown to be an effective self-supervised learning approach. It is well known that fine-tuning leads to better accuracy in-distribution (ID). 06824: The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets Nov 12, 2023 · Hello! Thank you for this excellent model & paper! I am interested in reproducing the linear probing results in the paper for ImageNet (using SGD). In this work, we propose and exam-ine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier 1st Linear probing (LP), 2nd Fine-tuning (FT) FT starts with the optimized linear layer (classifier). ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias towards structured probes thus reducing LP-OVOD: Open-Vocabulary Object Detection by Linear Probing: Paper and Code. In this paper we present in a unified way the analysis of several random variables related with linear probing hashing with buckets, giving explicit and exact trivariate generating functions in the combinatorial Dec 7, 2023 · Abstract page for arXiv paper 2312. When comparing the two pre-training methods, the CLIP model learns richer semantic information reflected by its su-perior linear probing performance on ImageNet-1K. Apr 4, 2025 · Developing effective world models is crucial for creating artificial agents that can reason about and navigate complex environments. Abstract This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detector must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. 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. To analyze linear probing, we need to know more than just how many elements collide with us. Zero-shot CLIP performs competitively against fully supervised Linear Probe on ResNet50 on a wide array of tasks (wins in 16/27 datasets). arXiv:2202. While there is a plethora of hash table data structures, hashing with linear probing is the most efficient one in many practical situations. 1. Conclusion We introduced LP++, a strong linear probe for few-shot CLIP adaptation. 10054v1 [cs. A new approach to the analysis ofhash table performance is presented. Which method does better? First introduced in 1954, the linear-probing hash table is among the oldest data structures in computer science, and thanks to its unrivaled data locality, linear probing continues to be one of the fastest hash tables in practice. Class-Adaptive Linear Probe (CLAP): constraining the learnt prototypes to remain close to zero-shot weights. A revisited zero-shot initialized Linear Probe (ZS-LP), tailored for CLIP-alike vision-language models. Refer to [3] for examples and more detailed discussion of the basic techniques. Abstract. To insert an element x, compute h(x) and try to place x there. However, recent studies have Linear probing is another approach to resolving hash collisions. Based on this, in the the rest of the paper, we will focus on the hashing of 32 and 64 bit keys in our comparison between different hashing scheme. Apr 1, 2017 · Request PDF | Understanding intermediate layers using linear classifier probes | Neural network models have a reputation for being black boxes. downstream tasks such as linear probing and few-shot trans-fer [2,6,8,13,25,26]. May 27, 2024 · Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective: Paper and Code. We propose a log-likelihood ratio (LLR) approach to analyze the comparative benefits of visual prompting and linear probing. However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of Mar 28, 2023 · Omg idea! Maybe linear probes suck because it's turn based - internal repns don't actually care about white or black, but training the probe across game move breaks things in a way that needs smth non-linear to patch At this point my instincts said to go and validate the hypothesis properly, look at a bunch more neurons, etc. However, this method has a In fact, for our primary application of linear probing, it is shown in [14] that we can first use plain universal hashing into a domain of size n, and then we only need to handle 32-bit keys. The method isfirst applied toanalyze thep rformance of the classical (cyclic) linear probing scheme, and the results areused to solve anoptimal storage allocation Already Carter and Wegman, in their seminal paper on uni-versal hashing, raised the question of extending their anal-ysis to linear probing. Apr 4, 2022 · Abstract. Both approaches complement nicely, and give a good insight in the relation between linear probing and random walks. , image classification. 04000: LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures Apr 5, 2023 · Two standard approaches to using these foundation models are linear probing and fine-tuning. 1 (b)) consists of a main classifier, an episodic linear prob-ing classifier, and a regularization term. Oct 25, 2021 · Using a linear probe, CLIP beats other models in a few-shot context (up to 16 instances), and interestingly its 0-shot approach beats few shots up to 4. The process works in three main steps: 1) The probe learns to recognize patterns in the AI's internal states that correlate with Mar 21, 2025 · The demand for efficient hash table algorithms continues to grow as modern computing environments grapple with increasing data volumes and evolving access patterns. However, we show in this paper that linear probing using a 2-wise independent hash function may have expected logarithmic cost per operation. A robust metric in this sense is one that consistently replicates the downstream performance ranking observed with a ground truth oracle across a set of pre-trained models. However, despite the widespread use of Jan 14, 2025 · In this paper, we exploit models obtained in Self-Supervised Learning (SSL) to mitigate the impact of noisy labels in FL. In this paper, we introduce a simple strategy to regular-ize the network to be immediately plausible for an episodic linear probing classifier. It demonstrates the discriminability of the visual representations in training. Linear probing freezes the foundation model and trains a head on top. A typical approach for OVOD is to use joint text-image embeddings of CLIP to assign box Nov 29, 2024 · View a PDF of the paper titled LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states, by Luis Ibanez-Lissen and 4 other authors In this paper, we focus on downstream classification tasks, employing the widely adopted linear probing protocol. PALP inherits the scalability of linear probing and the capability of enforcing language models to derive more meaningful representations via tailoring input into a more conceivable form. While deep supervision has been widely applied for task-specific learning, our focus is on 1 Introduction Hash tables are among most fundamental and widely used data structures. Linear Probing: Theory vs. Our method involves two optimization steps: (1) extracting pseudo-labels using CLIP zero-shot classification and (2) employing the pseudo-labels to train LP-CLIP. However, despite the widespread use of Feb 1, 2023 · Our paper proposes linear reward probing as an efficient method to evaluate the quality of pretrained representations in the RL setting, and demonstrates its positive correlation with downstream RL performance. This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detector must identify both seen and unseen classes in test images without labeled examples of the unseen classes in 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. É Probes cannot tell us about whether the information that we identify has any causal relationship with the target model’s behavior. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based representations Understanding network generalization and feature discrimination is an open research problem in visual recognition. , ImageNet) and transfer learning (TL) to various downstream datasets are commonly employed to evaluate the quality of visual representations learned via self-supervised learning (SSL). However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when Jul 30, 2023 · This paper especially investigates the linear probing performance of MAE models. Moreover, supervision models may collapse intra-class details → worse performance. Similar to a neural electrode array, probing classifiers help both discern and edit the internal representation of a neural network. Hashing with linear probing dates back to the 1950s and is among the most studied algorithms for storing (key, value) pairs. We use both a combinatorial approach, giving exact formulas for generating functions, and a probabilistic approach, giving simple deriva-tions of asymptotic results. collision! collision! collision! Jul 2, 2021 · View a PDF of the paper titled Linear Probing Revisited: Tombstones Mark the Death of Primary Clustering, by Michael A. However, despite the widespread use of In keeping with previous work [5], we will focus most of our discussion on ordered linear probing; however, as we shall see, all of the results in this paper also have natural analogues for a classical linear-probing hash table. These models usually mask out some patches of images and require the model. Carter and Wegman, in their seminal paper on universal hashing, raised the question of extending their analysis to linear probing. Practice In practice, we cannot use a truly random hash function Does linear probing still have a constant expected time per operation when more realistic hash functions are used? For chaining, 2-independence, or just “universality”, was enough How much independence is needed for linear probing? Apr 2, 2024 · In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. Bibtex Paper Supplemental Authors Akiyoshi Tomihari, Issei Sato Digital Object Identifier (DOI) 10. In this paper, we investigate a deep supervision technique for encouraging the development of a world model in a network trained end-to-end to predict the next observation. included in the Cloppe In linear probing, collisions can occur between elements with entirely different hash codes. Without losing generalizability, we mainly discuss MAE [17] in this paper. 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"). Jul 18, 2024 · A quick and practical guide to Linear Probing - a hashing collision resolution technique. Feb 6, 2025 · The probe fires far less on alpaca responses unrelated to deception, indicating it may partially be a probe for “deception-related” text rather than “actually-deceptive” text. However, despite the widespread use of large Linear Probing Linear probing is a simple open-addressing hashing strategy. More advanced approaches, including Cuckoo hashing linear_probe: If only training the last linear layer (freeze lower layers), set True, for full fine-tuning set False use_net_val_mode: True if you want to keep the network in "val" mode while training. Jun 27, 2025 · View a PDF of the paper titled Few-Shot Segmentation of Historical Maps via Linear Probing of Vision Foundation Models, by Rafael Sterzinger and 2 other authors Apr 7, 2023 · Linear probing (LP) (and k -NN) on the upstream dataset with labels (e. Apr 23, 2024 · Probing methods closely related to those used here were recently described under the banner of “linear artificial tomography” within the representation engineering research agenda. A key methodological Sep 19, 2025 · This paper studies how to probe (efficiently assess) the feature quality given a backbone model in the context of dense prediction, which differs from conventional paradigms such as linear probing wherein the entire features are assessed for, e. Yeah I think that makes sense, and it really plugs a lot of the holes and connects things to the linear/quadratic probing and also the clustering problem. Bidirectional linear probing l ts the algorithm probe up or down, depending on the value of th Apr 6, 2025 · On the other hand, linear probing, a standard transfer learning method, can sometimes become the best approach. However, we show in this paper that linear probing using a pairwise independent family may have expected {\em logarithmic} cost per operation. Although existing SSL methods have shown good performances under those evaluation protocols, we observe that the performances are very ABSTRACT near probing (updat-ing only the last linear layer—the “head”). Linear probing and class unconditional generation per-formance of different methods trained and evaluated on ImageNet-1K. However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of In this paper, we propose an episodic linear probing (ELP) classifier to reflect the generalization of visual representations in an online manner. Linear probing, often applied to the final layer of pre-trained models, is limited by its inability to model complex relationships in data. Fine-tuning updates all the parameters of the model. Bender and 2 other authors 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"). The regulariza-tion term considers the relation between the main classifier and the episodic linear probing classifier Jan 20, 2025 · Linear-probing hash tables have been classically believed to support insertions in time Θ(x2), where 1 − 1/x is the load factor of the hash table. The study examines the relationship between the model's feature space during linear probing and the optimization trajectory during fine-tuning. 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. Nov 16, 2021 · Researchers made a discovery related to linear-probing hash tables that could lead to more efficient data storage and retrieval in computers. 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. Technically, it analyzes the model's internal representations to detect when it's being overly agreeable rather than truthful. Linear probing is a scheme in computer programming for resolving collisions in hash tables, data structures for maintaining a collection of key–value pairs and looking up the value associated with a given key. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. Results linear probe scores are provided in Table 3 and plotted in Figure 10. 4. They show that linear probing creates an improved initialization state for fine-tuning. This is due to its simplicity, cache efficiency, absence of overhead for internally used Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Probes in the above sense are supervised 4. However, we show in this paper that linear probing using a pairwise independent family may have expected logarithmic cost per operation. It is shown in this paper that linear probing using a 2-wise independent hash function may have expected logarithmic cost per operation, and it is shown that 5-wise independence is enough to ensure constant expected time per operation. In this work, we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier Dec 8, 2006 · Already Carter and Wegman, in their seminal paper on universal hashing, raised the question of extending their analysis to linear probing. This approach isbased on a direct probabilistic analysis, where the underlying probabilities areder byved using theballot theorem andits ramifications. Traditional search techniques such as linear probing and quadratic probing are known to suffer from clustering effects, leading to performance degradation in high-load scenarios. The author proposes a method that is agnostic of the resolution of the feature. Abstract This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detec-tor must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. In recent years it has become one Sep 11, 2023 · View a PDF of the paper titled Two-way Linear Probing Revisited, by Ketan Dalal and 2 other authors The linear probe is trained in an unsupervised manner in a teacher-student setting. We propose a new method to understand better the Dec 1, 2024 · The linear probe functions as a diagnostic tool that identifies specific neural patterns associated with sycophantic behavior in LLMs. How-ever, its fine-tuning performance on most other tasks are worse than MAE, as shown in Tab. Zero-shot Linear Probe (ZS-LP): a surprisingly strong well-initialized Linear Probe. However, despite the Jan 16, 2024 · In this paper, we introduce LiDAR (Linear Discriminant Analysis Rank), a metric designed to measure the quality of representations within JE archi- tectures. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. However, this method has a critical Oct 10, 2023 · Abstract page for arXiv paper 2310. Final section: unsupervised probes. A typical approach for OVOD is to use joint text-image em-beddings of CLIP to assign box proposals to their closest text label. Changes to pre-trained features are minimized. In this paper, we focus on downstream classifi-cation tasks, employing the widely adopted linear probing protocol. In this paper, we introduce LiDAR (Linear Discriminant Analysis Rank), a metric designed to measure the quality of representations within JE architectures. 52202/079017-4436 Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. We compare a variety of probing methods. MIT PhD student William Kuszmaul led the research with Michael Bender of Stony Brook University and Bradley Kuszmaul of Google. Oct 5, 2016 · View a PDF of the paper titled Understanding intermediate layers using linear classifier probes, by Guillaume Alain and Yoshua Bengio ABSTRACT. Our metric addresses several shortcomings of recent approaches based on feature covariance rank by discriminating between informative and uninformative features. The idea behind linear probing is simple: if a collision occurs, we probe our hash table taking one step at a time until we find an empty spot for the object we wish to insert. Unlike separate chaining, we only allow a single object at a given index. Many studies have been conducted to assess the quality of feature representations. o 1 this probing technique provides good memory caching through good locality of reference. g. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. Dec 1, 2024 · View a PDF of the paper titled Linear Probe Penalties Reduce LLM Sycophancy, by Henry Papadatos and 1 other authors Oct 26, 2023 · This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detector must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. Our simple framework (Fig. A. Training on roleplaying scenarios and probing the activations after a follow-up question works well. If that spot is occupied, keep moving through the array, wrapping around at the end, until a free spot is found. The typical linear probe is only applied as a proxy Jun 22, 2024 · In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head during LP. A constraint formulation to retain prior knowledge of the robust zero-shot prototypes per class, CLass adaptive Linear Probing (CLAP). This paper evaluates the use of probing classifiers to modify the internal hidden state of a chess-playing transformer. dpybl sekoerk djseizk ybou igzrym nylal xfcd gzkptp uvmsw wbnp lyvyw uhahu rgarbiba vqqqyzi cpbdudeq