ImageNet

Timeline

1. Oct 19, 2026 · Opinion / analysis · 1 source
   Arrow detection with ConvNext pretrained on DINOv3 [P]
   I experimented a lot with resnets (pretrained on Imagenet) which performed quite well, pure ViT which performed worse.
2. Oct 16, 2026 · Research paper · 1 source
   Rethinking the design space of Vanilla Pixel Diffusion Transformers
   Pixel-space diffusion models have recently re-emerged as a compelling alternative to latent diffusion, avoiding the lossy latent bottleneck and separately trained autoencoder.
3. Oct 16, 2026 · Research paper · 1 source
   Escaping Parameter Space: Tight Generalization Bounds via Representation Quality
   We derive a tight generalization bound for deterministic neural We derive a tight generalization bound for deterministic neural networks based on their last hidden layer representations.
4. Oct 16, 2026 · Research paper · 1 source
   FedRAPP: Federated Test-Time Prompt Tuning for Vision-Language Models with Reliability-Aware Prompt Personalization
   Federated prompt learning enables vision-language models like CLIP to collaboratively adapt lightweight soft prompts across distributed clients without sharing raw data.
5. Oct 16, 2026 · Research paper · 1 source
   PONDER: Learning to Generate in the Right Order
   We introduce PONDER, a method that learns in which order an image generator should fill in content and which of its earlier predictions to revise.
6. Oct 16, 2026 · Research paper · 1 source
   Data-Efficient Classifier Pretraining Through Diffusion Models
   Motivated by these traditional approaches, we propose DiffPT, a simple diffusion modeling approach to pretrain a given classifier backbone for limited domain-specific training data.
7. Oct 16, 2026 · Research paper · 1 source
   A100: A Hybrid Vision Foundation Model for Agricultural Plant Image Analysis
   We propose A100, an agricultural vision foundation model that is pre-trained using self-supervised learning on 3 million unlabeled images from fields covering more than 100 crop species.
8. Oct 16, 2026 · Research paper · 1 source
   How Murky Before a VLM Knows It Cannot See? Accuracy, Confidence and Abstention on a Physical Visibility Axis
   We measure accuracy, internal (token-probability) confidence, verbalized confidence and an explicit abstain option on a visibility axis in metres: fog follows the Koschmieder model, an underwater arm scales the absorption spectrum of pure water, both are applied with a monocular metric depth map, and the visibility range V is swept over eight log-spaced levels from 250 m to 1.95 m.
9. Oct 16, 2026 · Research paper · 1 source
   Learning to Be Guided: Shaping Latent Geometry for Classifier-Free Guidance
   Classifier-free guidance (CFG) is a standard component of conditional diffusion models, and guided generation performance has become the de facto measure of state-of-the-art models.
10. Oct 15, 2026 · Research paper · 1 source
    Normalizing Flow Maps
    In this paper, we combine these threads of few-step generation and tractable sample likelihoods and introduce Normalizing Flow Maps (NFM), a class of exactly invertible pushforward maps that generalizes normalizing flows and flow maps.
11. Oct 15, 2026 · Research paper · 1 source
    Deep Learning for Deepfake Image Detection: Comparative Model Evaluation and Gradient-Based Visual Attribution
    This study examines an explainable deep-learning workflow for image-level deepfake detection using two contrasting classification strategies: a compact convolutional model trained from scratch and an ImageNet-pretrained MobileNetV2 model adapted through two-stage transfer learning.
12. Oct 15, 2026 · Research paper · 1 source
    Co2In: a Bi-level Memory Incremental Learning Framework with Knowledge Encoding, Consolidation, and Integration
    Inspired by human memory mechanisms, this paper introduces an IL framework with knowledge enCoding, Consolidation, and Integration (Co2In).
13. Oct 15, 2026 · Research paper · 1 source
    The Dormant Spiking Neuron: A State-Driven Mechanism for Efficient Spiking Neural Networks
    Spiking neural networks (SNNs) offer a compelling pathway toward energy-efficient artificial intelligence, fundamentally anchored in presynaptic input-driven sparsity.
14. Oct 15, 2026 · Research paper · 1 source
    ShiftQAT: Quantization-Aware Training for the Numerical Contract of a Fixed-Function Edge CNN Accelerator
    Fixed-function CNN accelerators make edge inference fast and energy-efficient, but they execute only networks that obey a precise numerical contract.
15. Oct 15, 2026 · Research paper · 1 source
    Federated Art Chase: Privacy Leakage and Data Poisoning in Federated Fine-Tuning
    Federated learning (FL) is increasingly used to adapt a pretrained, centrally fine-tuned model to data that remain on user devices, beyond training a model from scratch.
16. Oct 15, 2026 · Research paper · 1 source
    Phase Kernel Lifts Capacity of Dense Associative Memory
    Dense associative memory underlies modern attention mechanisms; however, Modern Hopfield Networks (MHNs) with linear kernels yield overlapping attractor basins and spurious states under high memory load, limiting their theoretically exponential storage capacity in practice.
17. Oct 15, 2026 · Research paper · 1 source
    CoViG: Coarsened Vision GNN for Image Classification
    Inspired by optimization methods for GNNs, we present CoViG, a novel approach combining graph coarsening and ViGs.
18. Oct 15, 2026 · Research paper · 1 source
    Clean Data Can Still Carry Backdoors: Support-Persistent Backdoors for Model Reuse
    In this paper, we propose DAR (Discover-and-Relabel), a support-persistent backdoor method that retains its behavior even after clean-data distillation.
19. Oct 14, 2026 · Research paper · 1 source
    Purify Before You Align: Improving Representation Alignment for Diffusion Models
    Representation alignment improves and accelerates diffusion training by encouraging intermediate features to match a pretrained visual encoder.
20. Oct 14, 2026 · Research paper · 1 source
    Generative Representation Learning for Scalable Latent Tokenization
    We introduce an approach for self-supervised representation learning designed to be used as a tokenizer for generative modeling.
21. Oct 14, 2026 · Research paper · 1 source
    One Size Does Not Fit All: Curvature-Aware Adaptive Perturbation for SAM
    Sharpness-Aware Minimisation (SAM) improves generalisation of deep neural networks by minimising a combination of the training loss and the local sharpness of the loss landscape, biasing convergence towards flat minima.
22. Oct 14, 2026 · Research paper · 1 source
    Are We Overthinking One-Step Generation?
    In this work, we introduce Priming Models, a simple two-stage framework for training one-step generators.
23. Oct 14, 2026 · Research paper · 1 source
    Rethinking Prediction Horizons: Decoupling Model Queries from State Updates in Few-Step MeanFlow
    MeanFlow predicts interval-average velocities, but conventional few-step sampling ties the interval queried from the model to the duration of the state update.
24. Oct 14, 2026 · Research paper · 1 source
    Adaptive Observation Control for Active Visual Recognition
    Visual recognition models usually process a fixed view under a fixed computation budget, although images often benefit from different scales and amounts of spatial evidence.
25. Oct 13, 2026 · Research paper · 1 source
    Data-free Multistep Diffusion Distillation
    One-step diffusion distillation achieves strong performance on small datasets but remains insufficient for high-resolution or complex text-to-image settings.
26. Oct 13, 2026 · Research paper · 1 source
    Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine
    The Continuous Thought Machine (CTM) is a recurrent network that attends to inputs and makes predictions based on the synchronization between its neurons.
27. Oct 13, 2026 · Research paper · 1 source
    A Gradient-Regularized Deep Learning Method for Restoring Image in Myopic Vision
    Myopic vision degrades the quality of human vision due to spherical eye defocus aberrations that impair the perception of fine spatial details.
28. Oct 13, 2026 · Opinion / analysis · 1 source
    Diffusion without CFG? Correction Training for Diffusion Models
    We test it on NanoGen, a unified diffusion transformer implementation for ImageNet and text-to-image generation.
29. Oct 13, 2026 · Research paper · 1 source
    Data Density Scaling Laws for Image Self-Distillation
    To address this gap, we propose a modified version of a Chinchilla-style scaling law that explicitly accounts for two factors: the number of unique samples and repetition per sample.
30. Oct 13, 2026 · Research paper · 1 source
    Dog-Breed Text-to-image Flow Matching from Pretraining to Fine-Tuning on 4 Consumer GPUs
    We develop MiniDog, a framework that enables pretraining diffusion transformers in 55 minutes and fine-tuning in another 15 minutes on four 3090 GPUs.

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